AGRICULTURE ISSUES AND POLICIES
ANIMAL FEED: TYPES, NUTRITION AND SAFETY
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AGRICULTURE ISSUES AND POLICIES
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ANIMAL SCIENCE, ISSUES AND PROFESSIONS
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AGRICULTURE ISSUES AND POLICIES
ANIMAL FEED: TYPES, NUTRITION AND SAFETY
SARAH R. BORGEARO EDITOR
Nova Science Publishers, Inc. New York
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LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Animal feed : types, nutrition and safety / editor: Sarah R. Borgearo. p. cm. Includes index. ISBN: 978-1-62100-862-0 (eBook) 1. Feeds. 2. Animal feeding. 3. Animal nutrition. I. Borgearo, Sarah R. SF95.A6316 2011 636.08'5--dc22 2010049654
Published by Nova Science Publishers, Inc. + New York
CONTENTS
Preface vii Chapter 1 Brown Algae as a Feed Additive: Nutritional and Health Impacts on Ruminant – A Review 1 Yuxi Wang and Tim A. McAllister Chapter 2 Animal Feeds and Nutrition Research: Conventional and Novel Approaches 33 Peiqiang Yu Chapter 3 Veterinary Drug Use and Environmental Safety 61 Sara Leston, Margarida Nunes, Marco F. L. Lemos, Gabriela Jorge da Silva, Miguel Ângelo Pardal and Fernando Ramos Chapter 4 Microbiological Safety and Quality of Animal Feeding Stuffs 85 Marijana Sokolovic Chapter 5 Effects of Selected Feed Compounds and Feed Additives on Gastrointestinal Tract Functions in Farm Animals; Health vs. Productivity Perspective 107 Violetta Naughton and Patrick J. Naughton Chapter 6 Application of Wavelet Neural Networks as a Non-Linear Modelling Technique in Food Microbiology 127 V. S. Kodogiannis, M. Amina, J. N. Lygouras and G. J. E. Nychas Chapter 7 The Effects of Natural Antioxidants Dietary Supplementation on the Properties of Farm Animal Products 155 Panagiotis E. Simitzis and Stelios G. Deligeorgis Chapter 8 A Risk Analysis of Compound Feed Contamination 169 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne Chapter 9 Use of Probiotics as Dietary Supplement in Cattle Goats and Pigs 183 Romina Ross, Ana Apás, Mario E. Arena and Silvia N. González vi Contents
Chapter 10 Nutritional Aspects of Theropithecus Gelada: From Wild-Feeding to Captive Animals 195 Marcus Mau, Jacinta Beehner and Achim Johann Chapter 11 Effect of Wilting and Plant Types on Physical and Chemical Composition of Silage 207 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede, J. A. Olanite and O. S. Onifade Chapter 12 Feed to Milk: New Tools for the Identification of Plant Species 219 Silvia Gianì, Anna Paola Casazza, Luca Braglia, Floriana Gavazzi and Diego Breviario Chapter 13 A Comparison Study to Determinate the Best Model for Computing Mean/Median Irregular Feed Particle Sizes of Coarsely Dry-Rolled Barley for Animal Nutrition Research 239 L. Du and Peiqiang Yu Index 247
PREFACE
This new book presents topical research in the study of animal feed, including conventional and novel feed research programs; veterinary drug use and environmental safety; microbiological safety and quality of animal feeding stuffs; the effects of selected feed compounds and feed additives on the gastrointestinal tract functions in farm animals and a risk analysis of compound feed contamination. Chapter 1 – Brown algae have been a component for ruminant diets in coastal areas for millennia. Recently, there has been a renewed interest in using brown algae in ruminant diets with an emphasis on the benefits of this feed to both animal and environmental health. Laboratory and animal studies have shown that brown algae or its extracts can reduce the shedding of bacterial pathogens, serve as an antioxidant, enhance immune function and moderate the impact of toxins on ruminants. Although the agents responsible for these biological responses remain to be fully elucidated, bioactive compounds such as phlorotannins and fucodians are almost certainly responsible for some of these responses. This chapter reviews recent research that has defined the effects of brown algae and their role in animal health, rumen microbiology, nutrient digestion, metabolism and overall animal productivity. Chapter 2 – Animal feed is the largest single cost (~60 to 75%) of production facing livestock feeding operations not only in Canada but also in the world. As Ministry of Agriculture Strategic Research Chair of Feed Research and Development, the authors feed research programs aim to develop new, high value feeds and new feeding applications and increase feed and livestock production efficiencies through improved livestock product quality reduce environmental impact and support sustainable animal production. Feed advancement is based on a new level of understanding and analytical information on feeds including the effect of intrinsic structures of feeds in relation to feed quality, digestive behavior and nutrient utilization. In this article, recently obtained information on their feed and nutrition research and methodology development is reviewed. The emphasis of this review is on both conventional and novel feed research programs and progress made in our research team, which are include: (a) Foreign gene-transformation to feeds to increase nutrient availability, (b) Feed heating processing to manipulate the nutrient digestive behaviour; (c) Fractionation of a feed to extract increased value from the feedstuffs, improve the competitive position of the livestock industry, and to increase economic returns. This article also reported that progress on traditional and non-conventional feed research methodology, including (a) Novel synchrotron-based bioanalytical techniques to study feed structures at cellular and molecular levels in relation to nutrient availability as well as viii Sarah R. Borgearo synchrotron-based molecular nutrition research and (b) Modeling nutrient supply to more accurately accounts for digestive processes in ruminants on a quantitative basis. The information described in this paper gives better insight in feed research progress and update. Chapter 3 – Presently, the aspects related with human nutrition are of significant importance, focusing the attention of scientists and policymakers regarding the safety of food supplies. Furthermore, the world‘s continuously growing population and the correlated decrease of natural resources have led to the steep increase of animal farming, especially aquaculture. To assure farmed animal‘s health the resort to chemicals and veterinary drugs is frequently taken. This practice in animal production is well described, whether for prophylaxis or therapeutical needs, which dictated the implementation of regulations regarding a responsible use of drugs and chemicals, to reduce the hazards (defined as ―a biological, chemical or physical agent in, or condition of, food with the potential to cause an adverse health effect‖ according to Regulation (EC) No. 178/2002) to consumers. However, the guidelines regarding the hazards of drug use contemplate farmed animals, veterinary drug residues, feeds, withdrawal time and the quality of the water used in aquaculture, neglecting the potential contamination of the surrounding natural ecosystems. Ecologically and economically important species may present similar risks to human health as farmed animals do as they can also accumulate drugs and chemicals, thus representing an increased threat as such species are not subjected to the same safety controls as are farmed animals. Also, the possibility of a given chemical or drug being accumulated along the food chains (biomagnification) is, in most cases, overlooked. In this chapter, the risks of environmental contamination and food safety will be addressed, in particular for species with significance to human consumption. Chapter 4 – The quality of animal feedingstuffs has paramount importance for the welfare of animals. Contaminated feed is one of the potential sources of infections in animals. Besides causing of nutrient losses it can also cause detrimental effects on animal health and production. Many countries all over the world have implemented national and international programs of monitoring and control of raw and processed feeds for animals. These controls regularly include testing for general microbiological safety (by determination of total aerobic count, presence of pathogenic bacteria of genera Salmonella, Staphylococcus, Clostridium, pathogenic strains of E. coli as well as for other potentially pathogenic bacteria species). This control also includes testing for the presence of yeasts and pathogenic moulds. Although yeasts and some bacteria and moulds in feed can have protective effects, other can cause spoilage of animal feed. Furthermore, moulds can produce secondary metabolites called mycotoxins and they are usually present in cereal grains, the major ingredient of the animal feed. Therefore, potential presence of pathogenic moulds in feed results with reduction of the availability of nutrients and adds an additional health risk because of the presence of the secondary metabolites – mycotoxins. Usually, processes for feed production are efficient in destroying of majority of undesirable yeasts and bacteria. However, proper use of raw substances, production and storage conditions are important factors in assuring feed of adequate quality. The aim of this chapter is to summarize the current situation of microbiological safety of animal feedingstuffs, impact of mycotoxins and other undesirable substances on safety of animal feedingstuffs and animals, and significance of potential biological contamination of Preface ix animal feedingstuffs and impact it has on food for human consumption as well as to give overview of recommendations concerning quality and safety of animal feedingstuffs. Chapter 5 – The composition of diets for farm animals, as well as their supplementation with different feed additives in order to obtain better performance, has evolved over past decades. However, the performance response to many feed compounds and additives, especially in growing animals, is often variable. Specific interactions between dietary components and the gastrointestinal tract (GI) functions can help us to understand the sources of this variability. Furthermore, a physiological approach towards animal diets may assist in formulation of the modern diets that on the one hand will assure animals‘ health but also will be financially viable. Thus the primary objective of this report is present the influence of selected feed compounds and feed additives on GI tract homeostasis. Specifically, to demonstrate the relationship between specific dietary protein and peptides, selected feed additives including feed antibiotics, amino acids, and feed acidifiers, as well as selected secondary plant products (plant lectins) on physiological functions of the gastrointestinal tract (including GI motility, gastric and pancreatic secretion, intestinal absorption, GI microflora) in animals, particularly during their growth and development. Chapter 6 – The need for intelligent methods to model highly nonlinear systems is long established. Feed-forward neural networks have been successfully used for modelling of non- linear systems. The main features of these systems such as the ability to learn from examples and to self-adapt are very well suited for the multi-resolution approach intrinsic to wavelets. Wavelets offer an adequate framework for the representation of ―natural‖ signals that are described by piece-wise smooth functions, with rather sharp transitions between neighbouring domains. The combination of wavelet theory and neural networks has lead to the development of wavelet networks (WNNs). The aim of this research study is to investigate the modelling capabilities of modified WNNs, where the connection weights between the hidden layer neurons and output neurons have been replaced by a local linear model, for describing the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in milk, and to evaluate its performance against classic neural network architectures and models utilised in food microbiology. Milk was artificially inoculated with an initial population of the pathogen of ca. 107 CFU/ml and exposed to a range of high pressures (350, 450, 550, 600 MPa) for up to 40 min at ambient temperature (ca. 25°C). Models were validated at 400 and 500 MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas all learning-based networks were utilised in a standard identification approach. The prediction performances of the proposed learning-based networks were better at both validation pressures. The development of accurate models to describe the survival curves of microorganisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process. Chapter 7 – Animal products are important sources for protein, fat, essential amino acids, minerals, vitamins and other nutrients in human nutrition. In recent years, the consumer demands for healthier animal products with favorable properties and improved quality are rapidly increasing worldwide. Antioxidants represent a group of compounds effective in improving quality characteristics of animal products by limiting the negative implications of lipid oxidation. Oxidation of lipids and the production of free radicals are natural processes occurring in biological systems leading to oxidative deterioration. Oxidative deterioration is x Sarah R. Borgearo initiated in the highly-unsaturated fatty acid fraction of membrane phospholipids, leading to the production of hydroperoxides, which are susceptible to further oxidation or decomposition to secondary reaction products such as short-chain aldehydes, ketones and other oxygenated compounds that may adversely affect lipids, pigments, proteins, carbohydrates vitamins and the overall quality of animal products by causing loss of nutritive value and limiting shelf- life. Oxidation destroys the membrane structure, disturbs transport processes and causes losses in the function of the cell organelles. In the past synthetic antioxidants such as butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), tert-butylhydroquinone (TBHQ), gallates were extensively used with the intention to delay, retard or prevent the negative effects of lipid peroxidation by scavenging chain-carrying peroxyl radicals or diminishing the formation of initiating lipid radicals. During the last decades interest in employing antioxidants from natural sources to increase the shelf life of foods is considerably enhanced by consumer preference for natural occurring ingredients and concerns about the possible toxic effects of synthetic antioxidants. The majority of natural antioxidants are phenolic compounds and the most important groups of them are the tocopherols, flavonoids and phenolic acids. The traditional practice of adding antioxidants during processing can still play a very important role since the added compounds have the potential for enhancing the activity of the inherent antioxidants systems. However, the dietary supplementation with antioxidants appears to be a more effective way of retarding lipid oxidation of animal products and controlling stability compared to post mortem addition of antioxidants. Dietary supplem- entation has been proved to be a simple and convenient strategy to uniformly introduce a natural antioxidant into phospholipid membranes where it may effectively inhibit the oxidative reactions at their localized sites. As a result, components of natural antioxidants are distributed, retained, and remained functional in animal products. On the other hand, no negative implications on animal products quality properties have been observed. Nowadays, there is a strong interest in isolating antioxidants from natural sources and incorporating them in animal nutrition with the intention to minimize lipid oxidation in products and to enhance their quality and nutritive value. However, further study is needed to elucidate their exact action and establish their regular use in animal production. Chapter 8 – By means of risk-financing instruments recall losses caused by contaminated compound feed can be pooled and transferred to other parties, i.e. the insurance industry. With knowledge of the occurrence of a contamination crisis and related damages, the probability distribution of the risk can be approximated. Reliable risk estimation is often hampered because of the lack of a complete claim distribution. Even when there would be a reliable claim data set, the relevance of such historical claims to modelling the future is dubious. The sophisticated quality assurance and tracking and tracing system which spans the entire production chain have reduced the risk of feed contamination substantially. Evidently given the often sparse data in the risk analysis of such food safety related issues it would be desirable to bring more information into the process of specifying the probability distribution. Inevitably, there must be much subjectivity in this process and there will be scope for disagreement on how best to proceed. By means of stochastic simulation the effects of elicited deviations of the ‗best guess estimates‘ for the insureds, insurers and re-insurers can be addressed. The revealed sensitivity of the outcomes enables to support the decision- making process in the design and pricing of a product recall insurance in a more transparent way. Preface xi
Chapter 9 – The application of potentially beneficial microorganisms to improve host defense is a new trend to increase health benefits. The authors developed the first specific host probiotics for goats and pigs from a mixture of lactic acid bacteria isolated from healthy animals. In both cases, the probiotic administration enhanced the body weight and was able to modify microflora balance by reducing Enterobacteria and increasing lactic acid bacteria population. The intestinal composition of fatty acids was modified, with a diminution of saturated fatty acid and an increase of the beneficial linoleic acid. On goats, the probiotics administration was correlated with a ten time diminution of fecal putrescine (cancer and bacterial disease marker) and a decrease of 60% mutagen fecal concentration, indicating the protective effect of this treatment. These results are encouraging towards the use of probiotic mixtures as functional food for goats and pigs. Chapter 10 – Extant wild populations of geladas (Theropithecus gelada) are endemic to the Ethiopian plateau with two major populations in the Simien Mountains National Park and at Guassa. Around 200 geladas are kept in 20 zoos worldwide, participating in a conservation- breeding-program. Geladas are the only primate species that feeds primarily on fiber-rich grasses. Most of these grasses include phytoliths, which prevent excessive feeding due to the amount of enamel abrasion they cause. In response to this abrasion, geladas have developed co-evolutionary compensation strategies such as high molar crowns to circumvent the mechanical defences of grasses. In the Simien Mountains, geladas take 65-82% of their diet from grass, with smaller contributions from herb roots (3-21%), herb leaves (6-16%), seeds (2-5%), fruits (0-3%), and very rarely, invertebrates (0-0.1%). Here, the grassland ecosystem has been heavily degraded by humans and their livestock. There are also reports on dwindling alpine meadows getting replaced by vegetation-compositions of lower altitudes as a result of global warming. At Guassa, which is a more intact tall grass ecosystem, geladas consumed 55% grass, 29% herb leaves, 8% herb roots, 3% invertebrates, and 5% other items. Thus, while their relative intake of grass parts and herbs varies substantially across sites, geladas still obtain at least half their diet from grass. Moreover, they were only recently observed to actively hunt on locusts at Guassa. Generally, geladas are able to digest more than 50% of the fiber of their daily rations. Based on this, it was hypothesized that gelada baboons are capable hindgut fermenters. Recent physiological research has demonstrated that gelada feces ferment cell wall material coming from grass. The intensity of in vitro gas production to measure microbial fermentation in geladas was similar to that of zebras and unexpectedly, not higher than in hamadryas baboons. This result implies that gelada baboons might not be dependent solely on grass. The consumption is supported by a very high expression of salivary amylase. Consistent with that, it has been observed at Simien National Park that geladas use starch-rich roots and seeds as alternative energy sources primarily during the dry seasons. As well, captive geladas tend to favor seeds over most other foods whenever offered. In conclusion, combined studies of food plants, digestive physiology, and evolutionary ecology could help to learn more about the gelada‘s unique feeding adaptation and to implement optimized dietary plans in zoological gardens worldwide. Chapter 11 – An experiment was conducted to evaluate the effects of wilting and plant types on physical and chemical composition of silage. Three forage grasses and two legumes were harvested at vegetative stage from the Fadama (wetland areas) located within the University. The grasses were Panicum maximum, Pennisetum purpureum and Cynodon nlemfuensis while the legumes were Stylosanthes hamata and Centrosema pubescens. The forage samples were chopped into pieces of 2-3cm in length. Half of the forage samples were xii Sarah R. Borgearo wilted in the open for 24 hours before ensiling; the other half was ensiled as fresh materials. The experiment was arranged in an 11 x 2 factorial experimental design with 3 replicates. A total of 66 anaerobic glass jars were used for the study. Wilting x plant species interactions were observed for some (colour and odour) of the characteristics of the silages. Generally, silages from grass/legume mixtures recorded better physical parameter scores than those of monocultures. Interactions was also observed for the dry matter (DM) (P<0.001) and pH (P=0.010) of the silages. Higher (P=0.003) DM contents were observed in silages made from wilted forages. On the contrast, higher (P>0.05) crude protein contents were observed in silages made from unwilted forages. Wilting did not have any negative effect on the fibre content of the silages. The pH values were within the range of pH for good quality silage. S. hamata silage recorded the highest CP content. Ensiling grasses and legumes together produced quality silages. Chapter 12 – Certification of feed quality, origin and composition has become a fundamental need all over the world. The nutritional value of the feed, the traceability of its components along the agri-feed chain and the safety of the productions are intrinsically supported by such a certification. Worldwide, we are witnessing to a steep-growing request for transparency and correct information about feed and feed-derived products that comes from government institutions, producers, feed- chain distributors and consumer associations. Recently, this request has been materialized in law-enforced rules that aim to grant the traceability of the products provided with the diet to the animals, their origin and their identification across the whole chain of production, in accordance with the farm to fork concept. Even more, the composition of the feed is a compulsory request for certifying the authenticity of those high-quality traditional and regional products, that are alternatively assigned the different PDO, PDS and TSG labels. Technology should efficiently support such a request. Investigation tools and procedures should ideally be simple, rapid, reproducible, affordable, versatile, exportable from lab to lab and capable of providing an easy and immediately comprehensible output. The authors have identified such tools in plant introns of both nuclear and plastidial origin. They are amplified principally by EPIC-PCR, a conceptually simple approach, providing an easily identifiable genetic bar code for plant species that are found in feed, forages, pasture grasses and milk. Here the authors report about the success of such innovative methods and their several advantages compared to most popular yet more laborious, more demanding and expensive techniques. By combining the results they can obtain in feed and milk, the whole chain of production can be reconstituted and genetically validated. Chapter 13 – Barley is a main diet ingredient for beef and dairy cattle in North American, particularly in Canada. The particle size distribution and mean and median of barley significantly affects nutrient degradation and availability. The barley is usually coarsely dry- rolled before feeding in North America. The shapes of coarsely dry-rolled barley particles are not round but very irregular. There are three models available in literature which could be used to determine the mean and median particle sizes of a feed with irregular shapes. However, which model is most suitable for coarsely dry-rolled barley is not known. This information is badly needed. The objective of this study was to determine which model was the best model to determine the particle sizes for coarsely dry-rolled barley fed to ruminants. Eighteen barley samples from three consecutive years were used in this study. The models that were compared included: (1) Fisher‘s model; (2) Pond‘s model with 0 mm=100%; (3) Pond‘s model without 0 mm=100%; (4) Model for Geometric Mean (GM). The results Preface xiii showed that RSS from the Pond‘s model with and without 0 mm = 100% were 68.66 and 68.62, respectively. Both values were significantly smaller (P<0.05) than the RSS from the Fisher‘s model (RSS= 363.21), indicating that the Pond‘s model was more suitable to model particle size data from coarsely dry-rolled barley grain than the Fisher‘s model. R2 values (P<0.001) continued to support the point that the Pond‘s models (R2= 0.9987, 0.9984) were better than the Fisher‘s model. Within the Pond‘s methods, no difference was found for RSS and R2 (P>0.05), but better potency was observed in the Pond‘s model with 0 mm = 100%, which included the observation of particles passing through the smallest sieve (0.58 mm). R2 for the Pond‘s model with 0 mm = 100% was 0.9987. The estimation of mean/median particle sizes from Fisher‘s model was larger than those from the Pond‘s and GM models, with GM giving the smallest particle size. In conclusion, the Pond‘s model with 0 mm = 100% was the best model to compute mean/median particle sizes of the coarsely dry-rolled barley expressed as percent cumulative weight oversize. The mean and median particle sizes determined from the Pond‘s model with 0 mm = 100% are best to be used for nutrient availability study in future.
In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 1-32 © 2011 Nova Science Publishers, Inc.
Chapter 1
BROWN ALGAE AS A FEED ADDITIVE: NUTRITIONAL AND HEALTH IMPACTS ON RUMINANTS – A REVIEW
Yuxi Wang and Tim A. McAllister Agriculture and Agri-Food Canada, Lethbridge Research Centre, Lethbridge, AB, Canada T1J 4B1
ABSTRACT
Brown algae have been a component for ruminant diets in coastal areas for millennia. Recently, there has been a renewed interest in using brown algae in ruminant diets with an emphasis on the benefits of this feed to both animal and environmental health. Laboratory and animal studies have shown that brown algae or its extracts can reduce the shedding of bacterial pathogens, serve as an antioxidant, enhance immune function and moderate the impact of toxins on ruminants. Although the agents responsible for these biological responses remain to be fully elucidated, bioactive compounds such as phlorotannins and fucodians are almost certainly responsible for some of these responses. This chapter reviews recent research that has defined the effects of brown algae and their role in animal health, rumen microbiology, nutrient digestion, metabolism and overall animal productivity.
1. INTRODUCTION
Seaweeds have been utilized for food and feed as well as ingredients in medicinal and cosmetic products for as long as humans have been associated with the sea. In the past, the use of seaweeds as animal feed was largely restricted to coastal areas where the distance to market was short. In the early and mid 20th century, there were numerous reports evaluating
Corresponding author: E-mail: [email protected]; Telephone: 403-317-3498; Fax: 403-382-3156. 2 Yuxi Wang and Tim A. McAllister the nutritive value of seaweeds as efforts to expand its use in livestock production intensified (Woodward, 1951; Chapman and Chapman, 1980; McHugh, 2003). Modern aquaculture has expanded the world production of seaweeds with approximately 83% of the world‘s annual production of 2.1 million metric tonnes being consumed by humans (McHugh, 2003). Seaweeds (also called macro-algae) refer to macroscopic plants that occur in the inter- tidal and sub-tidal regions and also in estuaries and mangroves of the ocean. In botanical terms, they also are known as marine algae. According to their pigmentation, seaweeds or macro-algae can be classified into three main categories: green (Chlorophyceae), red (Rhodophyceae) and brown (Phaeophyceae). Among the three groups, brown algae have attracted the most attention for their use in livestock production due to the bioactive nature of the plant secondary compounds that they contain. In the past, brown algae were noted for their high mineral and vitamin content (Chapman and Chapman, 1980) and they were often added to the diet for this purpose. More recently, Allen et al. (2001b) reviewed the beneficial antioxidant properties of brown seaweed (Ascophyllum nodosum) for ruminants fed high- endophyte forages. The current chapter reviews recent advancements in our knowledge of the nature and activity of bioactive compounds in brown algae and their extracts as well as their impact on gut physiology/microbiology and livestock productivity.
2. SOURCES, COMPOSITION AND NUTRITIONAL VALUE OF BROWN ALGAE
There are approximately 1,500-2,000 species of brown algae worldwide (Hoek et al., 1995), but only a small portion of these are utilized as livestock feed. Brown algae that are used for food and feed are mainly grown in cold waters of the Northern and Southern hemispheres with Laminaria, Undaria and Hizikia genera mainly for human consumption and Ascophyllum and Laminaria for animal feed. If surface seawater temperature continue to rise as a result of global climate change, the availability of brown algae as food or feed is also likely to increase (Ugarte et al., 2010). Brown algae contain large quantities of vitamins and minerals and are rich in carotenes
(20-170 ppm), vitamin C (500-3000 ppm) and other vitamins including B12 that is absence from most of terrestrial plants (Dharmananda, 2002). Seaweeds tend to have higher levels of macro and trace minerals (8–40%) as compared to many terrestrial plants, making them a rich source of these nutrients (Ito and Hori, 1989). In contrast, the protein content in brown algae is low (3-5% of dry matter [DM]) and lacks significant quantities of lysine or methionine (Ito and Hori 1989; Matanjun et al. 2009), amino acids that are typically the first-limiting to ruminant productivity. An unique characteristic of brown algae is the high content of iodine, ranging from 1500-8000 ppm in Laminaria spp and 500-1000 ppm in Fucus spp (Dharmananda, 2002). A summary of the nutrient composition of some of the brown algae is presented in Tables 1 and 2. Although brown algae are abundant in soluble fibres such as alginates, carrageenan and agar, they are indigestible by mammalian endogenous enzymes. The ability of microbes within the digestive tract to degrade these soluble fibres is also limited (Michel and Macfarlane, 1996; Michel et al., 1996; Wang et al., unpublished data), making these carbohydrates largely unavailable to the host. Brown Algae as a Feed Additive 3
Table 1. Approximate nutrient composition (% of DM) of some species of brown algae
Species CPa Lipid Ash Fibre NFE IVDMD A. nodosum 5-12 2.6-7.0 15-30 4-7 42-64 32.1 A. japonica 21.2 4.9 13.6 6.0 44.1 - F. serratus 5.0 2.0 13.1 5.5 62.0 14.6 F. spiralis 4-14 - 19-22 4.5* 21-38 47.5 L. hyperborean 5.9 0.8-1.7 13.7 3.6 63-68 - L. saccharina 6.4 0.7 16.7 3.3 59.4 56.4 L. digitata 5.8-9.0 0.6 11.3 26.7 36.6-47 58.7 L. japonica 8.2 1.2 19.6 3.3 58.2 - P. naliculata 4-13 3.6 15-22 1-2* 21-35 52.5 U. pinnatifida 15.0 3.2 30.8 9.2 35.3 - S. polycystum 5.4 0.3 42.4 8.5 33.5 - Sources: Chapman & Chapman (1980), Augier and Santimone (1978), Ito and Hori (1989), Paterson (1984), Conradt (1997), Woodward (1951), Dierick et al. (2009), Matanjun et al. (2009). *cellulose only. aCP, crude protein; NFE, N free extract; IVDMD, in vitro dry matter digestibility
Table 2. Major mineral and trace elements (mg/g dry weight) in some genera of brown algae
Mineral Fucus Laminaria Underia A. nodosum Na 54.7±0.60 38.2±0.43 70.6±1.66 25-30 K 43.2±0.46 115.8±1.28 87.0±1.44 15-25 Ca 9.4±0.07 10.1±0.05 9.3±0.38 15-20 Mg 9.9±0.13 6.6±0.06 11.8±0.34 5-10 S† 37.5±0.4 13.3±0.5 14.3±1.1 20-30 Fe 0.04±0.002 0.03±0.005 0.08±0.011 0.1-0.5 Zn 0.04±0.004 0.02±0.004 0.024±0 0.01-0.05 Mn 0.06±0.001 <0.005 0.009±0 0.001-0.005 Cu <0.005 <0.005 <0.005 <0.001 I 0.55±0.019 4.87±1.102 0.14±0.095 0.1-0.4 Adapted from Rupérez (2002), Dierick et al. (2009), Romaris-Hortas et al. (2010) and Acadian Seaplants Ltd. † Content for Fucus, Laminaria and Underia are sulphate (SO4 (2-).
Digestion of seaweed proteins, especially in monogastrics can also be hampered by the presence of phenolic compounds (phlorotannins) and high concentrations of soluble fibre (Fleurence, 1999; Wong and Cheung, 2003; Marrion et al., 2005). In addition, numerous secondary metabolites with wide diversity of biological activities and pharmaceutical functions have been found in brown algae (Harper et al., 2001), a list that continues to grow (Mayer and Hamann, 2005; Mayer et al., 2009). Particularly, brown algae contain unique polyphenolic compounds (phlorotannins) and sulphur-containing carbohydrates (fucoidans) that possess varying biological activities. 4 Yuxi Wang and Tim A. McAllister
3. PHLOROTANNINS
3.1. Occurrence, Chemical Structure and Quantifications
Phlorotannins occur only in marine brown algae and are formed as a result of the polymerization of phloroglucinol (1,3,5-trihydroxybenzene) (Ragan and Glombitza, 1986). Phlorotannins are well known to marine ecologists and their occurrence, structure and role in marine ecology has been the subject of numerous studies (Ragan and Glombitza, 1986; Steinberg, 1992; Amsler and Fairhead, 2006). However, the impact of phlorotannins on livestock nutrition or intestinal microbiology has not been defined. Phlorotannins are secondary metabolites of marine brown algae that are analogous to the tannins in terrestrial plants. They are mainly synthesized via the acetate-malonate pathway (Herbert, 1989) although other pathway has been proposed (Chen et al. 1997). These compounds are concentrated in the phsodes located in the cytoplasm of cells within the outer cortical layers of the thalli (Ragan and Glombitza, 1986; Lüder and Clayton, 2004; Shibata et al., 2004). Concentrations of phlorotannins in brown algae vary greatly depending on species, growth stage, growing environment and their exposure to marine herbivory (Ragan and Glombitza, 1986; Amsler and Fairhead, 2006). Under some conditions, phlorotannins can account for up to 25% of the DM of brown algae (Ragan and Glombitza, 1986; Steinberg, 1992; Targett and Arnold, 1998) and can be classified into six classes based on their chemical structure (Figure 1). The molecular mass of pholorotannins ranges from 126 Da to 650 kDa (Targett and Arnold, 2001).
Figure 1. Chemical structures of phloroglucinol and subunits of the six different structural classes of phlorotannins (Adopted from Amsler and Fairhead, 2006). Brown Algae as a Feed Additive 5
Phlorotannins are most commonly isolated from brown algae by aqueous extraction in methanol or acetone, resulting in the isolation of soluble and cell wall-bound fractions (Koivikko et al. 2005). Most studies have focused on the soluble phlorotannins which are quantified using colorimetry (Folin-Denis and Folin-Ciocalteu methods), gravimetry and protein precipitation (Ragan and Jensen, 1977; Steinberg, 1989; Stern et al., 1996b; Amsler and Fairhead, 2006; Parys et al., 2007). However, these methods quantify total extractable polyphenols and are not necessarily specific to phlorotannins. Stern et al. (1996a) overcame this limitation by developing a colorimetric assay that measured the density of a chromophore formed after the reaction of phlorotannins with 2,4-dimethyloxybenzaldehyde (DMBA). More recently, high performance liquid chromatography has been used to identify individual classes of phlorotannins (Shibata et al., 2002; Koivikko et al., 2008). However, as observed with terrestrial tannins (Ragan and Jensen, 1977; Amsler and Fairhead, 2006), chemical analyses of phlorotannins are not always reflective of their biological activity.
3.2. Biological Activities and Implication in Animal and Food Production Industries
Phlorotannins are an important component of the chemical defence system in brown algae (Ragan and Glombitza, 1986). They are suggested to have multiple roles in marine ecology such as anti-herbivory (Targett and Arnold, 1998; 2001; Pavia and Toth, 2000) and anti-fouling (Sieburth and Conover, 1965; Wikström and Pavia, 2004). Among the numerous biological activities and pharmaceutical applications of phlorotannins (Ragan and Glombitza, 1986; Amsler and Fairhead, 2006), their antimicrobial activity is likely the most significant in terms of their use in livestock production. Part of this potential arises from the broad- spectrum antibacterial activity that these compounds exhibit after isolation from a variety of brown seaweed species (Table 3). Escherichia. coli O157:H7, Salmonella, Hepatitis A, Campylobacter, Shigella, Norovirus and Listeria are food-borne pathogens of major public health concern in North America. A variety of foods, including poultry, eggs, meat, milk, fruits and vegetables, have all been implicated as vectors of one or more of these pathogens in outbreaks of food-borne illness (Dàoust, 1997; Doyle et al., 1997). The majority of the phlorotannins outlined in Table 3 possess antimicrobial activity against several of these food-borne pathogens, in particular E. coli O157:H7 and Salmonella. Work in our laboratory found that phlorotannin from A. nodosum exhibited bactericidal activity against a number of strains of E. coli O157:H7 at concentrations as low as 50 ppm, whereas 400 ppm of terrestrial tannins were required to achieve the same level of bactericidal activity (Wang et al., 2006, 2008, 2009a, c). This raises the possibility that phlorotannins could be an effective mitigation strategy against pathogens in food production and processing. Phlorotannins exhibit bactericidal activity against both Gram positive and Gram negative bacteria (Nagayama et al., 2002; Taskin et al., 2007), a characteristic that differs from terrestrial tannins which inhibit primarily Gram positive bacteria (Jones et al., 1994; Smith and Mackie, 2004). However, consistent with terrestrial tannins, the antibacterial activity of phlorotannins is correlated with their molecular size with low- to intermediate-molecular weights exhibiting greater activity than high molecular weight phlorotannins (Ragan and Glombitza, 1986). Nagayama et al. (2002) compared the antibacterial activity of 6 Yuxi Wang and Tim A. McAllister phlorotannins isolated from brown algae (Ecklonia kurome) to tea catechins using 25 strains of food-borne pathogenic bacteria, 9 strains of methicillin-resistant Staphylococcus aureus (MRSA) and one strain of Streptococcus pyogenes. It was found that the antibacterial activity of phlorotannins was greater than that of the catechins against all targeted bacteria.
Table 3. Antibacterial activity of some phlorotannins isolated from brown algae
Sources Sensitive bacteria References B. cereus, Methicillin-resistant Staphylococcus E. kurome aureus Nagayama et al., (2002) (Okamura) (MRSA), S. aureus, S. pyogenes, C. fetus, C. jejuni, E. coli, S. enteritidis, S. typhimurium, Vibrio parahaemolyticus Cymbella spp S. aureus, Corynebacterium diphtheriae, Al-Mola, (2009) (Diatoms) E. coli, Proteus mirabilis, Klebsiella pneumonia, S. typhi, Pseudomonas aeruginosa S. thunbeergii E. coli, P. aeruginosa, B. subtilis, Liu et al., (2007 (Kuntze) S. aureus, Botrytis cinerea, Penicillium expansum S. dentifolium B. subtilis, E. Coli, S. albus, S. fuecalis Shanab, (2007) Cystoseira barbata S. aureus, Micrococcus luteus, Enterococcus Taskin et al., (2007) faecalis,Enterobacter aerogenes, E. coli, E. coli O157:H7 A. nodosum Vacca and Walsh, 1954; E. coli, Pseudomonas, Micrococcus, Aerobacter, Ghoul et al., 1995; E. coli O157:H7, Brucella, Salmonella, Wang et al., (2009a, b, Klebsiella, Streptococcus, rumen bacteria (2010b ) Cladostephus S. aureus, E. coli O157:H7 Taskin et al., (2007 spongiosus F. verticillatus Gelidium elegans MRSA, B. subtilis, P. aeruginosa, V. Horikawa et al., 1999 Dictyopteris undulate parahaemolyticus Ishige okamurae E kurome, D. spinulosa, S. horneri E. cava MRSA, Salmonella spp Choi et al., (2010) L. digitata, L. L. monocytogenes, S. abony, E. faecalis, P. saccharina, Cox et al., (2010) aeruginosa Himanthalia elongate
The chemical factors responsible for the greater antibacterial activity of phlorotannins as compared to terrestrial tannins are unknown. Smith et al. (2003) identified oxidation of tannins and the liberation of hydrogen peroxide as one factor responsible for their antimicrobial properties. It has been shown that the amount of hydrogen peroxide produced differs among tannins with those rich in hydroxyl groups exhibiting the highest antimicrobial activity (Akagawa et al., 2003; Smith et al., 2003; Min et al., 2007). Additionally, tannins that possess more hydroxyl groups exhibit a greater affinity for proteins (Mueller-Harvey, 2006), a factor that could impede growth if bacterial cell surface proteins form complexes Brown Algae as a Feed Additive 7 with tannins (Wang et al., 2009a). Phlorotannins are readily oxidized upon exposure to air (Ragan and Glombitza, 1986) and contain more hydroxyl groups than terrestrial tannins. Indeed, Stern et al. (1996b) compared the protein oxidative activity of phlorotannins to terrestrial tannins and found that oxidative reactions were far more common among phlorotannins. Furthermore, phlorotannins that exhibit a high degree of phloroglucinol polymerization appear to possess the greatest anti-bacterial activity (Nagayama et al., 2002). Although the antimicrobial activity of phlorotannins has long been recognized, their effects on rumen bacteria have only recently been studied. Wang et al. (2010b) have examined effects of phlorotannins isolated from A. nodosum on the three cellulolytic and four non-cellulolytic bacteria and found that pure cultures of these bacteria were very sensitive to phlorotannins (Figure 2).
A
B
Figure 2. Effects of phlorotannins from Ascophyllum nosdum on the growth of ruminal bacteria in pure culture. (a). non-cellulolytic bacteria (Streptococcus bovis, Selenomonas ruminantium, Prevotella bryantii and Ruminococcus amylophilus); (b). cellulolytic bacteria (Fibrobacter succinogenes, Ruminococcus flavefaciens and Ruminococcus albus). All bacteria have been pre-exposed to phlorotannins for 10 day (Wang et al., 2010b). 8 Yuxi Wang and Tim A. McAllister
Figure 3. Effects of phlorotannins from A. nodosum on the growth of ruminal bacteria in mixed culture. Cellulolytic bacteria were the sum of F. succinogenes + R. albus + R. flavefaciens and non-cellulolytic bacteria were the sum of P. bryantii + R. amylophilus + S. ruminantium + S. bovis (From Wang et al., (2009b).
In mixed ruminal culture, the effect of phlorotannins on ruminal bacteria was species- dependant. For example, phlorotannins at 500 ppm inhibited the growth of Fibrobacter succinogenes, had little influence on Ruminococcus flavefaciens and Ruminococcus albus, but promoted the growth of Selenomonas ruminantium, Streptococcus bovis, Ruminobacter amylophilus, and Prevotella bryantii. Overall, phlorotannins reduced the population of cellulolytic bacteria, but increased the populations of non-cellulolytic and total bacteria (Figure 3). Different effects on rumen bacteria in pure culture vs. mixed culture may reflect the influence of rumen environment on the biological activity of phlorotannins. Whilst the effects on rumen bacteria obtained from pure culture reflect the direct action of phlorotannins with bacteria, in mixed culture phlorotannins may interact with other non-bacterial targets such as feed protein and/or carbohydrates. Besides the impact of phlorotannins on microbial populations, there is evidence that feeding brown algae or brown algae extracts reduces feed intake, N retention, N utilisation and digestion in swine, possibly due to phlorotannins (Van Heugten, 2000; DeLange, 2000; Lee, 2008; Gardiner et al., 2008). Detailed experiments to examine the impact of phlorotannins on ruminal fermentation in vivo have not been conducted. However, our laboratory (Wang et al., 2006, 2008, 2009b) has evaluated the effects of phlorotannins from A. nodosum on the ruminal fermentation of forage mixture and barley grain in vitro. The results demonstrated that phlorotannins at concentrations up to 500 µg/mL linearly reduced the fermentation of both forage- and grain-based diets. Phlorotannins readily formed complexes with proteins and reduced amino acid deamination, as evidenced by a linear reduction in concentrations of ammonia-N with increasing concentration of phlorotannins. The inhibition of in vitro ruminal fermentation by phlorotannins was greater with a mixed forage than with a concentrate diet, suggesting that cellulolytic bacteria may be more Brown Algae as a Feed Additive 9 sensitive to pholortannins than amylolytic bacteria, a result that is supported by the lack of celluloytic bacteria in sheep consuming seaweed on Orkney Island (Orpin et al., 1985).
4. FUCOIDANS
4.1. Occurrence, Chemical Structure and Quantifications
The principal fibre components in brown algae are non-starch polysaccharides including alginates (intercellular mucilages), laminaran (storage polysaccharide) and fucans (cell wall components) that comprise up to 40% of the algae DM (Lahaye, 1991; Nishimune et al., 1991). Fucans are heterogeneous polysaccharides that are rich in L-fucose and sulphate and can be classified into three major groups: fucoidans (homofucans), xylofucoglycuronans (fucoglucuronans), and glycuronofucogalactans (glucuronogalactofucans) (Table 4). Fucoidans are widely present among all brown algae, but are absent from green, red, golden, and freshwater algae as well as terrestrial plants (Berteau and Mulloy, 2003). The core region (or backbone) of fucoidan is composed primarily of a repeating chain of fucose with intermittent fucose or sulfate branches (Figure 4).
Figure 4. Common structures of some fucoidans isolated from brown algae (Adapted from Berteau and Mulloy, 2003). 10 Yuxi Wang and Tim A. McAllister
Table 4. Chemical and physicochemical characteristics of soluble algal polysaccharides (adapted from Michel and Macfarlane, 1996)
Polymers Origin Cellular Chemical Molecular Physicochemical (main species) location composition weight properties (kDa) Alginates All brown Cell wall 1 ,4-ß-D- 20-80 Water-soluble seaweeds Mannuronic (some salts only), (Macrocystis Viscous Gelling acid, 1,4-a-L- spp., Laminaria agents, Cationic guluronic acid spp.) exchange capacity 1,3-ß-D-Glucose, Water-soluble All brown 1,6-ß-D-glucose, (some are Laminarans seaweeds Cytoplasm 1,3,6-ß- 2-4 temperature Laminariaceae) D-glucose and dependent) mannitol Neutral polymers All brown Fucans seaweeds Cell wall Fucoidans: Water-soluble 1,2-α-L-fucose-4- (Fucaceae) OSO3, Non-viscous 3-OSO3 or galactose or High cationic
exchange xylose or mannose or capacities
uronic acid ;
Glycuronofucogalactans: 78-150
1,4-ß-D-galactose,
L-fucose 3-OSO3;
Xylofucoglucuronans:
1,4-ß-D-mannuronic
acid, 3-ß-xylosyl-L-
fucose-3-OSO3
Fucoidans are easily extracted from the cell wall with hot water (Percival and Ross, 1950) or an acid solution (Black, 1954). The fucoidan content of seaweeds varies from as little as 2% in the Laminariaceae family to more than 20% in Fucaceae family depending on growing location and season (Black, 1954). Zvyagintseva et al. (1999) reported concentrations of fucoidans in L. japonica, L. cichorioides and F. evanescens ranging from 6 to 15% of the algae DM with molecular weights ranging from 19-38 kDa for L. cichorioides to 150-500 kDa for F. evanescens. In A. nodosum, fucoidan accounted for about 10% of the Brown Algae as a Feed Additive 11
DM (Baardseth, 1970). The fucoidan molecule contained about 38.3% SO4, 56.7% of L- fucose and 8.2% mineral (Chapman and Chapman, 1980). Fucoidan from Himanthalia and Pelvetia canaliculata were more complex containing 57% fucose, 4% galactose, 15% xylose, 3% uronic acid and 35% fucose, 3% glucose, 22% galactose and 6% arabinose, respectively (Chapman and Chapman, 1980). Surprisingly, there is more information on the structural nature of fucoidans than there is on their actual levels in various algae species.
4.2. Biological Activities and Implication in Animal and Food Production Industries
Fucoidans possess a variety of biological activities including anthelmintic, anti-bacterial, anti-coagulant, anti-fungal, anti-inflammatory, anti-malarial and antiviral properties (Table 5). Unique biological and pharmaceutical activities of these compounds continue to be identified (Mayer et al., 2009) and there are several extensive reviews on the structure and function of these compounds (Berteau and Mulloy, 2003; Mayer and Hamann, 2005; Mayer et al., 2009). Although fucoidans are water soluble, enzymes required to degrade these compounds (i.e., fucoidanase, α-fucosidase and sulphatase) exist mainly in marine organisms (Kitamura et al., 1992; Daniel et al., 1999; Berteau et al., 2002). Neither endogenous enzymes in terrestrial animals or microorganisms residing in their digestive tract seem able to degrade them (Salyers et al., 1977a, b; Michel et al., 1996). McNaught et al. (1954) found that mixed rumen bacteria were unable to utilize fucoidan or its building unit, L-fucose. They also hypothesized the structure of fucose was not amendable to degradation by ruminal microbial enzymes. Our preliminary in vitro results also showed that mixed ruminal and fecal bacteria were unable to ferment fucoidans extracted from A. nodosum and that the capacity to degrade these compounds was not induced by allowing the organisms to adapt to this substrate (Wang et al., unpublished). Although fucoidans do not appear to be digested in the rumen, the possibility that they may be absorbed within the lower intestinal tract of ruminants cannot be discounted. Li et al. (2005) reported that fucoidan extracted from L. japonica was toxic when fed to rats at levels above 300 mg/kg body weight. However, estimated fucoidan intake in studies using ruminants (e. g Tables 6 and 8) or swine (e .g. Gardiner et al., 2008; Dierick et al., 2009) were all well below 300 mg/kg body weight. Information on the effects of fucoidans on intestinal microbial ecology is limited. Most of the reported studies used whole grown algae or their extracts containing fucoidans and are limited to swine (Gahan et al., 2009; Reilly et al., 2008). If fucoidans are not degraded in the rumen, their effects on intestinal microbial ecology in ruminants could be similar to that of monogastrics. Shibata et al. (2003) showed that fucoidan from Cladiosiphon inhibited Helicobacter pylori attachment to porcine gastric mucin in vitro and reduced the prevalence of H. pylori in gerbils in vivo. Reilly et al. (2008) observed that supplementing 1.5 g/kg diet of L. hyperborea and L. digitata extract alone or in combination to weaned piglets reduced enterobacteria, bifidobacteria and lactobacilli populations in the caecum and colon. They also observed that inclusion of L. hyperborea extract resulted in an increase in the total number of monocytes. However, piglets that were fed L. hyperborea or L. digitata extracts alone or in combination did not exhibit differences in the total number of neutrophils, lymphocytes, or the phagocytotic capacity of leukocytes as compared to controls. This indicates that supplementation of brown algae extract had only marginal impacts on immune responses. 12 Yuxi Wang and Tim A. McAllister
Table 5. Observed biological activities of fucoidans from various brown algae species
A. nodosum Anticomplementary activity; Blondin et al., 1994; anticoagulant; Nardella et al., 1996; antiproliferative on vascular Logeart et al., 1997; smooth muscle cells Chevolot et al., 1999 Increasing cellular immunity; Maruyama et al., 2003; U. pinnatifida antiviral Lee et al., 2008 F. evanescens Anticomplementary activity Zvyagintseva et al., 2000 Laminariale S. schröederi Supporting gentamicin and Araújo et al., 2004 (xylofucoglucuronan) amikacin immobilization D. menstrualis (heterofucan) Anticoagulant Albuquerque et al., 2004 A. utricularis Antiviral Ponce et al., 2002 S. lomentaria Antioxidant Kuda et al., 2005 C. okamuranus Preventing H. pylori Shibata et al., 1999, 2003 infection and reducing the risk of associated gastric cancer F. vesiculosus Criado and Ferreirós, 1983, Neuroprotective effects 1984; Baba et al., 1988; Beress et against Aß toxicity; al., 1993; Moen and Clark, 1993; anticogulant; inhibiting Nishino et al., 1994; Bartlett et bacteria binding to microtiter al., 1994; Vázquez-Freire et al., plates; antiviral; antiparasite; 1996; Clark et al., 1997; Štyriaka antioxidant; lowering LDL et al., 1999a,b; Iqbal et al., 2000; cholesterol levels; lowering Jhamandas et al., 2005; Rocha de blood glucose levels; anti- Souza et al., 2007; Díaz-Rubio et inflammatory; antibacterial al., 2009 L. brasiliensis Anticogulant Mourão and Pereira, 1999 E. kurome Anticogulant Nishino et al., 1999 S. horneri Antiviral Hoshino et al.,1998 Zhao et al., 2005; L. japonica Antioxidant Wang et al., 2010a S. polycystum Antiviral, antibacterial Chotigeat et al., 2004 C. okamuranus Anti-tumor Fukahori et al., 2008 Ortega-Barria and Boothroyd, Fucoidan (Sigma) Antiparasite 1999 Fucoidan (Sigma; Inhibiting sperm binding to Milan, Italy) oviductal monolayers Talevi and Gualtieri, 2001
In this study, growth rate, feed intake and feed conversion rate as well as digestibility of nutrients were not affected by either extract (Reilly et al., 2008). An extract from Laminaria Brown Algae as a Feed Additive 13 spp containing laminarin (112 g/kg), fucoidan (89 g/kg) and ash (799 g/kg) was reported to be an potential alternative to lactose in high-lactose diets (>60 g/kg) fed to piglets after weaning (Gahan et al., 2009).
Table 6. Effect of Ascophyllum nosdum on Escherichia coli O157:H7 shedding in feedlot cattle
Extract Feedlot Grain based 0, 10 or Reduced fecal and hide Behrends et cattle diet (20 g/kg diet, E. coli and E. coli al., 2000 14 days prior to O157:H7 slaughtering) Reduced both fecal Grain based and hide E. coli Barham et Meal Cattle 20 g/kg DM diet O157:H7 and al., 2001 Salmonella Reduced enterohemorrhagic 20 g/kg diet, Feedlot Corn-based E.coli Braden et al., Meal 14 day pre- cattle diet O157:H7prevalence 2004 slaughtering on hide swabs and in fecal samples E. coli Barley 10 or (20 g/kg Reduced fecal O157:H7 based Bach et al., Meal diet, up to 14 shedding of E. coli challenged concentrate 2008 days) O157:H7 cattle diet
Table 7. Effect of supplementation of Ascophyllum nosdum at levels of 0 (Control), 1 (AN1), (2 (AN2) and 4 (AN4) mg/mL on the survivability of Escherichia coli O157:H7 during 48 - h ruminal incubation (values are mean of triplicate vials)
Treatments Log CFUa/mL of supernatant after 500 g centrifugation Forage diet 0 h 2 h 4 h 8 h 12 h 24 h 48 h Control 4.53 4.39 4.50 4.21 3.77 1.37 1.27 AN1 4.64 4.49 4.40 4.26 3.97 2.29 1.46 AN2 4.58 4.64 4.46 4.33 4.12 1.76 1.41 AN4 4.79 4.68 4.76 4.62 4.45 2.38 1.18
SEMb 0.081 0.128 0.071 0.076 0.102 0.341 0.280
Concentrate diet Log CFU/mL of whole culture 5.52 5.31 4.97 Control 5.72 5.62 5.52 5.31 4.97 4.21 3.28 AN1 5.73 5.66 5.57 5.35 5.03 4.37 3.57 AN2 5.72 5.65 5.56 5.29 5.02 4.26 3.66 AN4 5.72 5.65 5.53 5.30 5.07 4.35 3.85 SEM 0.011 0.012 0.007 0.014 0.027 0.074 0.045 a CFU: Colony forming unit. b SEM, Standard error of means.
Table 8. Animal response to added Ascophyllum nosdum or its water soluble extract
Sources Animals Diets Levels Observed effects References Extract Steers Endophyte infected 3.4 kg /ha No effects on growth; increased meat marbling score; Allen et al., 2001a tall fescue pasture alleviated some of negative effects of tall fescue toxicity.
No effect on growth; alleviating adverse effects of Endophyte infected Extract Steers 3.4 kg /ha endophyte onimmune function; improving hair coat Saker et al., 2001 tall fescue pasture condition in cattle grazing infected fescue. Extract Steers Endophyte infected 3.4 kg /ha Increased antioxidant activity in both the tall fescue pasture Fike et al., 2001 forage and the grazing ruminant. Extract Lambs Endophyte infected 1.7, or Increased daily gains, serum vitamin A and whole-blood Se. Fike et al., 2001 tall fescue pasture 3.4 kg /ha
Endophyte infected Improved color stability and extend beef shelf-life, Montgomery et al. Extract Steers 3.4 kg /ha tall fescue pasture particularly in cattle grazing. infected tall fescue 2001,
Endophyte-infested Enhanced immune function and protected against prolonged Extract Lambs tall fescue-based hay 10 g/kg diet heat-induced oxidative stress. Saker et al., 2004. Increased ruminal and total tract OM digestibility; increased Meal Steers Switchgrass hay 10 g/d slow degradable CP fraction; increased CP degradability; no Leupp et al., 2005 effect on intake.
Hay, including No effect on N metabolism; no effect on meat sensory endophyte infected 3.0 kg/ha characteristics; may alter lipid metabolism; no effects on Fike et al., 2005 Extract Lambs fescue (Low N) 10g/kg diet heat stress. Meal Steers Switchgrass hay 2.0 g/d Had beneficial effects on forage digestibility in low quality Leupp et al., 2005. forage. Meal Cows Total mix ration 56.7 g/d/head No effect on feed intake, respiration rate, rectal or rear Cvetkovic et al., udder surface temperature; increased milk and milk protein 2005. production of heat stressed cows. Meal Steers Corn based diet 20 g/kg diet No effect on growth; improved overall carcass merit Anderson et al., (marbling, quality grade) 2006.
Sources Animals Diets Levels Observed effects References Feeding (2 weeks prior to transport lowered body Meal Lambs Total mixed ration 2% of DMI temperature during hot periods of transport; lowered antibody production and adrenal function. Archer et al., 2007 Short-term supplementation increased carcass quality and Meal Steers Corn based diet 20 g/kg diet prolonged retail shelf life. Braden et al., 2007 Reduced transportation stress (by increasing anti-oxidant Kannan et al., Extract Goats Alfalfa pellets 20 g/kg diet status); no effect on body weight change or blood 2007a, b metabolites levels during transportation stress. Corn-alfalfa-based 30 g meal or 3 g Increased IgG and IgM antiboby; increased white cell, Meal Lambs Archer et al., 2008 pellet fucodian/day/head eosinopjils and lymphocyte counts; fucodian had no effect Meal Lambs Barley based 10 or (20 g/kg No effect on feed intake, growth rate or feed efficiency. Bach et al., 2008 concentrate diet diet ) Reduced DMI of steers exposed to heat stress; no effects on Meal Steers Total mix ration 1% of DMI growth; short-term effect (3 to 4 d) in lowering the rectal Williams et al., 2009 temperature during early exposure to heat stress. No effect on growth; (2% reduced carcass length and tail-fat Meal Lambs Total mixed ration 1 and (2% content; no effect on carcass weight Tavasoli et al., 2009
Maintained lower core body temperature, rump and ear Meal Cows Total mixed ration 2 and 5 g/kg DM Pompeu et al., 2010 temperature under heat stress; no effects on milk production
16 Yuxi Wang and Tim A. McAllister
The anti-parastic, antioxidant and anti-bacterial properties of fucoidan have been investigated by several groups (Table 5). Medina (2002) showed that carrageens (a sulphated polysaccharide extracted from seaweed) inhibited the attachment of E. coli to collagen/laminin in animal tissues by 71 to 99%, thereby reducing carcass contamination. Given the similar structure of carrageens to fucoidans and the ability of carrageens to inhibit E. coli adhesion, it is reasonable to assume that fucoidans may limit the establishment of E. coli O157:H7 and other pathogens in the hind gut by inhibiting their attachment to intestinal mucosa.
5. FEED VALUE OF BROWN ALGAE AS RUMINANT FEED
Woodward (1951) and Chapman and Chapman (1980) have reviewed the early literature on seaweed as animal feed in relation to animal performance. In general, results from feeding brown algae to animals as main feed component were contradictory and depended on the type of animal, diet and species of brown algae as well as their concentrations in the diet. Hansen et al. (2003) compared the feeding value of a L. digitata and L. hyberborea mixture as sole feed source to North Ronaldsay sheep that were pre-exposed or non-exposed to the same seaweeds. They found that both pre-exposed and non-exposed sheep had similar feed intake as compared to a mixed hay diet. Although they also observed that these seaweeds had a high in situ DM and organic matter (OM) digestibility ( 83%) , after 96-h of in situ incubation, this property was mainly attributed to the high solubility of the material as both endogenous and microbial enzymes were unable to degrade some of these soluble polysaccharides. This raises the question as to if the high in situ degradability of brown algae is a true reflection of its nutritive value. One observation that deserves comment was that inclusion of polyethylene glycol, that specifically binds and deactivates polyphenolics, markedly increased the fermentability of the algae for both pre- and non-exposed sheep (Hansen et al., 2003). This suggests that the polyphenolics in these seaweeds had negative impact on the ruminal digestion of the nutrients and pre-exposure of rumen microbes to phlorotannins did not alter microbial sensitivity to phlorotannins. This is consistent with the observation in previous rumen fermentation studies by Wang et al. (2008, 2010b). Black (1955) also showed that the digestibility of A. nodosum meal was 29.7% and 26.2%, whereas it was 66.2% and 71.0% with Laminaria meal for sheep and swine, respectively. Whitternore and Percival (1975) observed that swine fed diets containing 50% of a alginate-free extract of A. nodosum developed diarrhoea within 7 days and that the digestible energy and crude protein values of the A.nodosum residue was 2.2 MJ and -30 g/kg DM, respectively. Extraction of alginate may have lowered the energy value of the product and the low protein value may reflect the concentration of phlorotannins within the extract. This work suggested that A. nodosum should not be used as a main dietary component for monogastric livestock due it low energy and protein digestibility, which is supported by the work of Jones et al. (1981). We have conducted an in vitro experiment to assess the effects of treatment of dried A. nodosum with NaOH or H2O2- NaOH on its fermentation in a mixed forage diet that contained 40% seaweed. The results showed that diets containing either treated or non-treated A. nodosum had considerable less gas produced than diet containing no A. nodosum over 30 h of incubation (Figure 5). Brown Algae as a Feed Additive 17
Figure. 5. Gas production of forage diet containing none or 40% of A. nodosum (AN),ammonia treated AN (AAN), NaOH treated AN (NAN) or NaOH-H2O2 treated AN (NHAN) during a 30-h in vitro ruminal fermentation (Wang and McAllister unpublished).
Both chemical treatments increased gas production, a result that was paralleled when these chemicals were used to treat cereal straw (Homb, 1984). Since phlorotannins are alkali liable (Ragan and Glombitza, 1986) and fucoidans are easily oxidized, it would be expected that much of the negative effect of phlorotannins on the ruminal fermentation as discussed in previous section would be overcome upon chemical treatment.
6. BROWN ALGAE AS FEED INGREDIENT/ADDITIVE TO ALTER DIGESTIVE TRACT MICROBIAL POPULATIONS
6.1. Gastrointestinal Microorganisms
Besides phlotaninns and fucoidan as discussed above, there are many other bioactive compounds in brown algae including fatty acids, terpenes, carbonyls, and bromophenols that possess antimicrobial activity (Rosell and Srivastava, 1987; Schnitzler et al., 2001; Siamopoulou et al., 2004). Mayer and Hamann (2005) and Mayer et al. (2009) have compiled detailed lists of potential biological active compounds in seaweed. Therefore, when whole brown algae are fed to livestock, its effect on intestinal microbial populations is likely a reflection of the net impact of these compounds on microbial activity. McNaught et al. (1954) observed that rumen bacterial activity was greatly reduced either by incubating A. nodosum alone or in combination with maltose. Orpin et al. (1985) compared microbial populations within the rumen of sheep fed either grass pasture or a mixture of seaweed (a mixture of A. esculenta, L. saccharina, L. digitata or A. nodosum). The rumen populations in the pasture- fed sheep were similar to those of other domestic ruminants fed terrestrial plants, but those 18 Yuxi Wang and Tim A. McAllister within the rumen from sheep fed seaweed exhibited major differences in the dominant species. Total ciliate populations were quantitatively similar between the two however, in the seaweed-fed sheep Dasytricha ruminantium was one of the most dominant species. No phycomycete fungi or cellulolytic bacteria were found in the seaweed-fed animals, and the bacterial population was dominated by S. bovis, S. ruminantium, B. fibrisolvens and lactate- utilizing species. The absence of fungi and cellulolytic bacteria in the rumen of seaweed-fed sheep is likely due to the presence of phlorotannins and other anti-microbial compounds in brown algae as discussed above. To date there is no information available with regard to the effects of feeding brown algae on intestinal bacterial populations in ruminants. However, recently there have been several studies published on the effects of A. nodosum and extracts of A. nodosum, L. hyperborean and L. digitata on digestive parameters and on the intestinal microbial populations of pigs (Gardiner et al., 2008; Reilly et al., 2008; Dierick et al., 2009; Gahan et al., 2009). All studies showed that either intact algae or their extracts reduced E. coli populations in the hind gut and improved gut health. Although the extracts contained both laminarin and fucoidan, they only accounted for a portion of the extract. Interestingly, McDonnell et al. (2010) observed that although laminarin can serve as a substrate for intestinal bacteria, it reduces intestinal E. coli populations. This suggests that laminarin is not used as a substrate by E. coli, or it promotes changes in microbial ecology that are unfavourable for E. coli to persist. This prebiotic response is less likely to occur in the lower tract of ruminants as many of the bioactive compounds in seaweed would be subject to microbial degradation in the rumen.
6.2. Microbial Pathogens in Gastrointestinal Tract
Dietary intervention has been proposed as a promising method in mitigation of food- borne pathogens in beef cattle (Callaway et al., 2003; Jacob et al., 2009). Researchers have showed that supplementation of A. nodosum or its extract reduces the prevalence of E. coli O157:H7 on hides and in fecal samples of feedlot cattle (Behrends et al., 2000 Barham et al., 2001 Braden et al., 2004 Bach et al., 2008; Table 6). Braden et al. (2004) suggested that the reduction of pathogens was due to a direct antimicrobial effect of the seaweed product, but did not identify the cause. Another challenge study by members of our team showed that feeding A. nodosum meal decreased the shedding of E. coli O157:H7 in cattle (Bach et al., 2008). However, all of these reported studies did not identify the site within the digestive tract where brown seaweed acted upon E. coli O157:H7. We have conducted two in vitro experiments to determine the effect of A. nodosum supplementation on the survivability of E. coli O157:H7 during ruminal fermentation, one with mixture of alfalfa and grass hay (50:50) and another with a barley based diet (20% barley silage, 75.9% rolled barley grain and 4.2 % mineral and vitamin supplement). A. nodosum meal was supplemented at the levels of 0, 1, 2 and 4 mg/mL in both diets. Inclusion of A. nodosum at levels up to 4 mg/mL in the culture did not affect the survivability of E. coli O157:H7 over a 48 h in vitro incubation period (Table 7), an outcome previously reported by Swerdlove (2003). Therefore, it seems that the observed anti-E. coli O157:H7 activity of A. nodosum in animal experiments is unlikely to occur in the rumen. Brown Algae as a Feed Additive 19
It has been suggested that the rectal-anal junction is the major site of E. coli O157:H7 in the intestinal tract (Naylor et al., 2003). As A. nodosum has been shown to reduce E. coli populations in the hind gut of monogastric livestock it may cause a similar reduction in E. coli O157:H7 within the lower tract of cattle. There is no information on the effects of feeding brown algae on the fecal shedding of other food-borne pathogens, although in vitro studies showed that their extracts or purified compounds possess strong anti-microbial activity against range of foodborne pathogens (Table 3, 5).
7. IMPACT ON IMMUNE FUNCTION, HEALTH AND PRODUCTION PERFORMANCE
A. nodosum is by far the most abundant and accessible brown algae with the majority of research targeted at defining its effects on immune function, health and production performance of livestock. Allen et al. (2001b) have reviewed previous studies and summarized that A. nodosum and/or its extracts have been shown to enhance immune function in beef and improve the marbling score and extend the shelf-life of meat. Many of these responses have been observed in cattle grazing endophyte-infected tall fescue. Supplementation of A. nodosum extract to cattle grazing infected fescue has been shown to reduce with oxidative stress through improved immune function and vitamin E, Cu, and Se status (Allen et al. 2001b).
7.1. Effects on Immune Function and Production Performance of Ruminants Expereiencing Heat Stress
Saker et al. (2004) evaluated the effects of two forms of A. nodosum extract (Tasco- Forage and Tasco-EX) either applied to foliage of endophyte-infected tall fescue before harvest or included in with the forage to heat-stressed wether lambs. These extracts were shown to enhance the antioxidant status and immune function. Lambs fed the extract had increased red and white blood cell counts as well as enhanced activity of glutathione peroxidase and superoxide dismutase with reduced lipid hydroperoxide metabolites. However, further work showed that the A. nodosum extract did not alter rumen function as indicated by no changes in pH, ammonia concentration, VFA profiles, or apparent OM and N digestibility (Fike et al., 2005). Spiers et al. (2004) determined that 1% of the A. nodosum extract in the diet also temporarily lowered internal rectum temperature by 0.5oC in steers consuming endophyte- infected tall fescue that were exposed to a heat challenge (26-33oC). Reductions in feed intake and respiration rate were also observed. Authors offered the explanation that high levels of iodide in the extract may have reduced thyroid activity resulting in a decrease in metabolic heat production and core temperature. Cvetkvic et al. (2005) undertook a study to assess the effect of A. nodosum meal on lactation performance of Holstein cows experiencing heat stress. Before the experiment started, cows were first supplemented with 113 g/head/day of A. nodosum meal for one week, followed by 56 g/head/day for two weeks, a level that was continued until the completion of 20 Yuxi Wang and Tim A. McAllister the experiment. The results showed cows fed A. nodosum meal produced more milk with higher protein levels than cows that were not fed A. nodosum. However, supplementation of A. nodosum meal did not reduce respiration rate, rectal temperature or rear-udder skin temperature and had no effect on feed intake. This suggested that the improved milk production observed with A. nodosum meal was not mediated by a reduction in heat stress. In contrast, Pompeu et al. (2010) observed that cows supplemented with A. nodosum meal at 0.25% of the diet had a more moderate increase in rectal, rump and ear surface temperature, but similar milk production as cows that did not receive A.nodosum meal. Williams et al. (2009) also showed that inclusion of A. nodosum meal in the diet (1%) reduced DMI of heat- stressed steers and appeared to lower reticulum temperature of steers for a period of 3-4 days, but did not affect rectal temperature.
7.2. Effects on Immune Function and Production Performance of Ruminats under Transportion Stress
Transportation and pre-slaughter holding (usually without food and water) causes a considerable amount of stress to livestock. The effect of A. nodosum extract on stress due to pre-slaughter transportation and holding was assessed by Kannan (2007a, b). Goats were fed a diet containing 2% A. nodosum extract for 3 weeks prior to pre-slaughter transportation (6 h) and holding (overnight). The results showed that transportation and over-night holding resulted in live weight loss and increased creatine kinase activity and elevated plasma cortisol levels and neutrophil/lymphocyte ratios. Including A. nodosum extract in the diet did not influence the plasma concentrations of urea nitrogen, non-esterified fatty acid and glucose, creatine kinase activity, cortisol levels neutrophil, lymphocyte, monocyte counts or the neutrophil/lymphocyte ratio. In addition, A. nodosum decreased eosinophil count and lipid peroxidation, but increased glutathione peroxidase activities. The author suggested that seaweed extract supplementation may help goats to combat the effects of processing-related stressors through minimizing oxidative stress and increasing anti-oxidant activity. Archer et al. (2007) also observed that feeding A. nodosum meal at a rate of 20 g/kg of DM to lambs for 2 weeks prior to transport lowered body temperature during transport, but it also lowered antibody production and appeared to lower adrenal function. Plasma protein, albumin, calcium, phosphorus, glucose, blood urea nitrogen, aspartate amino transferase, magnesium, sodium, potassium and chloride concentrations post- transport were also linearly decreased as the dietary level of A. nodosum meal increased from 0.5 to 2%. This response was not observed in lambs that were not subject to transport related stress. It needs to be pointed out that despite these observed effects of supplementation of A. nodosum on stress related parameters (Allen et al., 2001b) little effort has been made to elucidate the mechanisms responsible for these responses. The anti-oxidant activity of phenolics and an increase serum vitamin E levels have been credited for the ability of A. nodosum to improve the immune competence of livestock (Allen et al., 1997; Fike et al., 2001; Montgomery et al., 2001). Hwang et al. (2010) documented anti-oxidative and immune-stimulating activities in a hot-water extract from a brown algae (Sargassum hemiphyllum) using four different in vitro antioxidant activity testing systems and two cell culture assays. Archer et al. (2008) compared the effects of A. nodosum with fucoidan, salts and betaine on the body temperature of lambs during transportation in hot weather. They Brown Algae as a Feed Additive 21 found that none of the components examined appeared to be responsible for the observed decline in body temperature with A. nodosum meal. This suggests that the combined activities of antioxidants in A. nodosum meal are responsible for the overall modulation of body temperature. The same total antioxidant activities of brown algae are also likely responsible for the observed improved carcass and meat quality and prolonged shelf life of meat (Allen et al., 2001a; Montgomery et al., 2001; Anderson et al., 2006; Braden et al., 2007). Responses may differ between aqueous and alkali extract as extraction under alkali condition generates a variety of novel and biologically active compounds that are not obtained in water extract (Craigie, 2010).
CONCLUSION
Although 2.1 million metric tonnes of brown algae are harvested annually, only a fraction of it is used as animal feed. However, there is a growing interest in the use of brown seaweed as feed additive for livestock. Brown seaweed has been shown to contain a number of unique biologically active compounds that may reduce the shedding of pathogens and improve the immune status of livestock. Of these compounds, many of these responses can be attributed to phlorotannins and fucoidans. Further work to characterize the structural diversity and biological activities of these compounds may provide new insight into the value of brown seaweed as a feed additive for livestock.
ACKNOWLEDGMENTS
The authors would like to thank Krysty Munns for her editorial comments and input into the chapter. Funding of the author‘s research program from Acadian Seaplants Ltd., Ontario Food Safety Research Program and the Alberta Funding Consortium, and the Agriculture and Agri-Food Canada peer-reviewed program is also gratefully appreciated.
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Vacca, D. D. and Walsh, R. A., (1954). The anti-bacterial activity of an extract obtained from Ascophyllum nodosum. J. Am. Pharm. Assoc., 43, 24–26. Van Heugten, E. V. (2000). Mycotoxins and other anti-nutritional factors in swine feeds. In, Swine Nutrition, second ed. (Lewis, A.J., Lee Southern, L., Eds.), CRC Press Inc., FL, pp. 563–578. Vázquez-Freire, M. J., Lamela, M. and Calleja, J. M. (1996). Hypolipidaemic activity of a polysaccharide extract from Fucus vesiculosus L. Phytother. Res., 10, 647–650. Wang, J., Zhang, Q., Zhang, Z., Song, H. and Li, P. (2010a). Potential antioxidant and anticoagulant capacity of low molecular weight fucoidan fractions extracted from Laminaria japonica. Int. J. Biol. Macromol., 46, 6-12. Wang, Y., McAllister, T. A. and Bach, S. J. (2006). A crude extract of Acadian seaweed: effects on in vitro ruminal fermentation and on Escherichia coli O157:H7. Can. J. Anim. Sci., 86, 598 (Abstr). Wang, Y., Xu, Z., Bach, S. J. and McAllister, T. A. (2008). Effects of phlorotannins from Ascophyllum nodosum (brown seaweed) on ruminal digestion of forage and concentrate diets in vitro. Anim. Feed Sci.Technol., 145, 375-395. Wang, Y., Xu, Z., Bach, S. J. and McAllister, T. A. (2009a). Sensitivity of Escherichia coli O157:H7 to seaweed (Ascophyllum nodosum) phlorotannins and terrestrial tannins. Asian-Aust. J. Anim. Sci., 22, 238-245. Wang, Y., Alexander, T. W. and McAllister, T. A. (2009b). In vitro effects of phlorotannins from Ascophyllum nodosum (brown seaweed) on rumen bacterial populations and fermentation. J. Sci. Food Agric., 89, 2252-2260. Wang, Y., McAllister, T. A., Ominski, K. H. and Krause, D. O. (2009c). Evaluation of the inhibitory effects of various tannins on the growth of E. coli O157:H7. J. Anim. Sci., 87, E-Suppl. 2. P31. Wang, Y., Yanke, L. J., Xu, Z. and McAllister, T. A. (2010b). Effect of hlorotannins from brown seaweed on ruminal bacteria. J. Anim. Sci., 88, E-Suppl. 2, pp 132. Whitternore, C. T. and Percival, J. K. (1975). A Seaweed residue unsuitable as a major source of energy or nitrogen for growing pigs. J. Sci. Food Agric., 26, 215-217. Wikström, S. A. and Pavia, H. (2004). Chemical settlement inhibition versus postsettlement mortality as an explanation for differential fouling of two congeneric seaweeds. Oecologia., 138, 223–230. Williams , J. E., Spiers , D. E., Thompson-Golden , L. N., Hackman , T. J., Ellersieck , M. R., Wax , L., Colling , D. P., Corners , J. B. and Lancaster, P. A. (2009). Effects of Tasco in alleviation of heat stress in beef cattle. Prof. Anim. Sci., 25, 109–117. Wong, K. H. and Cheung, P. C. K. (2003). Effect of fiber-rich brown seaweeds on protein bioavailability of casein in growing rats. Int. J. Food Sci. Nutr., 54, 269-279. Woodward, N. (1951). Seaweeds as a source of chemicals and stock feed. J. Sci. Food Agric., 2, 477-487. Zhao, X., Xue, C., Cai, Y., Wang, D. and Fang, Y. (2005). The study of antioxidant activities of fucoidan from Laminaria japonica. High Technol. Lett., 11, 91-94. Zvyagintseva, T. N., Shevchenko, N. M., Popivnich, I. B., Isakov, V. V., Scobun, A. S., Sundukov, E. V. and Elyakova, L. A. (1999). A new procedure for the separation of water-soluble polysaccharides from brown seaweeds. Carbohydr. Res., 322, 32–39. 32 Yuxi Wang and Tim A. McAllister
Zvyagintseva, T., Shevshenko, N., Nazarova, I., Scobun, A., Luk'yanov, P. and Elyakova, L. (2000). Inhibition of complement activation by water soluble polysaccharides of some far-eastern brown seaweeds. Comp. Biochem. Phys. C., 126, 209-215.
In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 33-59 © 2011 Nova Science Publishers, Inc.
Chapter 2
ANIMAL FEEDS AND NUTRITION RESEARCH: CONVENTIONAL AND NOVEL APPROACHES
Peiqiang Yu Department of Animal and Poultry Science College of Agriculture and Bioresources, The University of Saskatchewan Saskatoon, Canada, S7N 5A8
ABSTRACT
Animal feed is the largest single cost (~60 to 75%) of production facing livestock feeding operations not only in Canada but also in the world. As Ministry of Agriculture Strategic Research Chair of Feed Research and Development, Our feed research programs aim to develop new, high value feeds and new feeding applications and increase feed and livestock production efficiencies through improved livestock product quality reduce environmental impact and support sustainable animal production. Feed advancement is based on a new level of understanding and analytical information on feeds including the effect of intrinsic structures of feeds in relation to feed quality, digestive behavior and nutrient utilization. In this article, recently obtained information on our feed and nutrition research and methodology development is reviewed. The emphasis of this review is on both conventional and novel feed research programs and progress made in our research team, which are include: (a) Foreign gene-transformation to feeds to increase nutrient availability, (b) Feed heating processing to manipulate the nutrient digestive behaviour; (c) Fractionation of a feed to extract increased value from the feedstuffs, improve the competitive position of the livestock industry, and to increase economic returns. This article also reported that progress on traditional and non- conventional feed research methodology, including (a) Novel synchrotron-based bioanalytical techniques to study feed structures at cellular and molecular levels in relation to nutrient availability as well as synchrotron-based molecular nutrition research and (b) Modeling nutrient supply to more accurately accounts for digestive processes in
Corresponding author: Peiqiang Yu, Ph.D. Ministry of Agriculture Strategic Research Chair, College of Agriculture and Bioresources University of Saskatchewan, 6D10 Agriculture Building, 51 Campus Drive, Saskatoon. Canada, S7N 5A8. Tel: +1 306 966 4132. Fax: +1 306 966 4150. E-mail: [email protected] 34 Peiqiang Yu
ruminants on a quantitative basis. The information described in this paper gives better insight in feed research progress and update.
Keywords: Feed Utilization and Availability, Nutrition, Gene Transformation, Modeling Nutrition Supply, Feed Fractionation, Feed Processing, Conventional and Non- conventional Feeds, Molecular Nutrition, Molecular Structure
1. INTRODUCTION
1.1. Why Feed Research So Important? Feed Research Motivation and Background
Feed is the largest single cost (up to 75%) of production facing livestock feeding operations not only in Canada but also in the world. As Ministry of Agriculture Strategic Research Chair of Feed Research and Development, we set up general goals to develop or assist to develop new, high value feeds and new feeding applications for Saskatchewan crop ingredients and increase feed and livestock production efficiencies through improved livestock product quality, reduce environmental impact and support sustainable animal production. Advancements will be based on a new level of understanding and analytical information on feeds including the effect of intrinsic structures of feeds at cellular and molecular levels in relation to feed quality, digestive behavior and nutrient utilization and availability. New knowledge gained from the research program will result in the development of nutritional strategies that will increase the efficiency of nutrient use in animals. The information obtained from our programs will aid in feed-crop breeding programs for selecting superior varieties and/or for prediction of feed-crop quality and nutritive value for animals. The strategic research program will benefit the animal, feed, feed processing and feed-crop industries in Saskatchewan and enable development and commercialization of research technologies developed in Saskatchewan and world-wide to extract increased value from our feedstuffs, improve the competitive position of our livestock industry, and to increase economic returns to Saskatchewan.
1.2. Conventional and Non-Conventional Feedstuff and Feed Research Areas
In general, we are working in following four major areas:
Area 1: To assist industry to develop Saskatchewan-based feed products and evaluate new feed and feed ingredients to support industry feed development and commercialization. Area 2: To assist to develop low cost feeds to support the development of the Saskatchewan livestock industry. Area 3: To improve feed quality and market value of existing feeds and investigate feed inherent structure (at cellular and molecular levels), biological components matrix and nutrient interaction in relation to feed utilization and nutrient availability. Animal Feeds and Nutrition Research 35
Area 4: To aid in feed/food-crop breeding programs for selecting superior varieties and/or for prediction of feed-crop quality and nutritive value for animals.
To achieve our general goals, several different feeds/nutrition research and development programs under federal government, provincial government and industry funding have been carried out. The detailed activities and research results are summarized in the following sections.
1.3. Objectives of This Article
In this article, recently obtained information on our feed and nutrition research and methodology development is reviewed. The emphasis of this review focus on both conventional and novel feed research programs and the progress made in our research team, which are include: (a) Foreign gene-transformation to feeds to increase nutrient availability, (b) Feed heating processing to manipulate the nutrient digestive behaviour; (c) Fractionation of a feed to extract increased value from the feedstuffs, improve the competitive position of the livestock industry, and to increase economic returns. This article also reported that progress on traditional and non-conventional feed research methodology, including (a) Novel synchrotron-based bioanalytical techniques to study feed structures at cellular and molecular levels in relation to nutrient availability, (b) Synchrotron-based molecular nutrition research, and (c) Modeling nutrient supply to more accurately accounts for digestive processes in the ruminant on a quantitative basis. The information described in this article gives better insight in feed research progress and update.
2. TRADITIONAL AND NOVEL FEED RESEARCH METHODOLOGY
2.1. Basic Feeds Chemical Profiling
In a feed evaluation, no mater whether it is a traditional feed or non-traditional feed or a novel feed, the first study is a basic feed chemical composition profile study. The following chemical compositions are often analyzed when we evaluate a new feed. Before chemical analysis, feed samples are normally ground through a 1 or 0.5 mm screen. Dry matter (DM), ash, crude fat (CFat) or ether extract (EE) and crude protein (CP) contents area usually analyzed according to the procedures of the AOAC (AOAC, 1990). The starch content in a feed is analyzed using the Megazyme Total Starch Assay Kit and by the α-amylase/amy- loglucosidase method (McCleary et al., 1997). The acid detergent fiber (ADF), neutral detergent fiber (NDF) and acid detergent lignin (ADL) values are often analyzed (Van Soest et al., 1991). In fiber analysis, we should indicate whether sodium sulfite and heat stable α- amylase are used or not prior to neutral detergent extraction. The N adjusted NDF (NDFn) is calculated as: NDF-NDICP (NRC, 2001). The acid (ADIN) and neutral detergent insoluble N (NDIN) values are determined according to the method published by Licitra et al. (1996). The non-protein N (NPN) content is analyzed by precipitating of true protein with tungstic acid (samples are soaked into water with 0.3 M Na2WO4 for 30 minutes) and 36 Peiqiang Yu calculated as the difference between total N and the N content of the residue after filtration (Licitra et al., 1996). Total soluble crude protein (SCP) is determined by incubating the sample with bicarbonate-phosphate buffer and filtering through Whatman #54 filter paper (Roe et al., 1990). The non-structural carbohydrates (NSC) including starch, sugars, organic acids, and other reserve carbohydrates such as fructan are estimated by non-fibre carbohydrates and calculated (NRC, 2001). The carbohydrate (CHO) and true protein, hemicellulose, and cellulose are calculated (NRC, 2001; Van Soest et al., 1991). Sometime, feed samples are also analysed for acid insoluble ash (AIA) using 20 g/kg HCl acid according to the procedure published by Van Keulen and Young (1977) and concentration values of AIA in feeds can be used to calculate the apparent DM digestibility. Several important minerals are also analysed. Calcium (Ca) concentrations are determined by atomic absorption spectroscopy (Model Perkin Elmer 2380, Norwalk, CT) and phosphorus (P) concentrations by spectrophotometry (Model Pharmacia LKB Biochrom Ltd, Ultroscope III, Cambridge, UK) according to the procedures of the AOAC (1990). Sulphur (S) analysis is carried out by inductively couple plasma-optical emission spectrometer (Model Perkin Elmer Optima 4300 DV ICP-OES, Waltham, MA) according to the procedures of the AOAC (199).
2.2. Feed Energy Profiling
The 2nd study to evaluate a feed that we usually do is feed energy profiling study. Normally, gross energy value (GE) in a feed sample is determined with the use of a bomb calorimeter (Parr 1281, Parr Instruments Company, Moline, Illinois). Energy contents for total digestible CP (tdCP), fatty acid (tdFA), NDF (tdNDF) and NFC (tdNFC) and total
digestible nutrient at 1× maintenance (TDN1X), digestible energy at a production level of intake (DE3x, 3× maintenance intake), metabolizable energy at a production level of intake (ME3x, 3× maintenance intake) and net energy for lactation at a production level of intake (NEL3x, 3× maintenance intake) are determined using a NRC summative approach from the NRC-2001 dairy (NRC, 2001), while net energy for maintenance (NEm), and net energy for growth (NEg) are determined using the NRC-1996 beef (NRC, 1996; NRC, 2001). Both NRC-2001 dairy and NRC-1996 beef use the same formula to estimate NEm and NEg in a feed.
2.3. Feed Protein and Carbohydrate Subfractions
The current CPM model (diet formulation) needs protein and carbohydrate fractions data from a feed (CPM-Dairy, 2010). The crude protein and carbohydrate subfractions are partitioned according to the Cornell Net Carbohydrate and Protein System (CNCPS) (Sniffen et al., 1992: Chalupa and Sniffen, 1994). The characterization of the CP fractions as applied in this system is as follows: fraction PA is non-protein N (NPN), fraction PB is true protein (TP), and fraction PC is unavailable protein. Fraction PB is further divided into three fractions (PB1, PB2, and PB3) that are believed to have different rates of degradation in the rumen.Buffer-insoluble protein minus fraction PB3 is used to estimate fraction PB2. Fraction PB2 is insoluble in buffer but soluble in neutral detergent, while fraction PB3 is insoluble in Animal Feeds and Nutrition Research 37 both buffer and neutral detergent, but is soluble in acid detergent solution. Fraction PB2 is fermented in the rumen at a lower rate than the buffer-soluble fraction, and some PB2 fraction escapes to the lower gut. Fraction PB3 is believed to be more slowly degraded in the rumen than fractions PB1 and PB2 because of its association with the plant cell walls; a large proportion of PB3 is thus believed to escape the rumen. Fraction PC is ADIN, which is highly resistant to breakdown by microbial and mammalian enzymes, and it is assumed to be unavailable to the animal. The relative rumen degradation rates of the five protein fractions have been described (Sniffen et al., 1992) as follows: fractions PA is rapidly degradable with an assumed degradation rate to be infinity, fraction PB1 is rapidly degradable with a degradation rate of 120-400 % h-1, fraction PB2 is intermediately degradable and has a degradation rate of 3-16% h-1, fraction PB3 is slowly degradable with a degradation rate of 0.06-0.55% h-1, and fraction PC is unavailable and considered to be undegradable. Carbohydrate is partitioned into: rapidly degradable fraction (CA) which is composed of sugars that have a rapid degradation rate of 300% h-1, intermediately degradable fraction (CB1) which is starch and pectin with an intermediate degradation rate of 20-50% h-1, slowly degradable fraction (CB2) which is available cell wall with a slow degradation rate of 2-10% h-1, and an unfermentable fraction (CC) which is the unavailable cell wall. A revised CNCPS protein and carbohydrate fraction models further partition feed CA fraction into four subfractions CA1 to CA4 (Tylutki et al., 2008; Jonker et al. 2010).
2.4. In Vitro Feed Study
2.4.1. In Vitro Rumen Degradability or Total Digestibility The short (eg. 24) and long (eg 48 h) in vitro rumen degradability of nutrient components such as DM (IVDMD24 and IVDMD48), ADF (IVADFD24 and IVADFD48) and NDF (IVNDFD24 and IVNDFD48) of feeds are usually determined using the Tilley and Terry (1963) procedure (Barnes and Marten, 1979; Marten and Barnes, 1980). The incubated residues are normally dried in the forced-aired oven at 55C for 24 h and then ground through a 1 mm screen for chemical analysis (Yu et al., 2003a). This method is often used in forage feed quality evaluation.
2.4.2. Three-Step In Vitro Study on Feed Intestinal Digestion of Rumen Undegraded Protein The estimation of intestinal digestion is usually determined by the three-step in vitro procedure described by Calsamiglia and Stern (Calsamiglia, S. and Stern, M. D. 1995). Briefly, dried ground residues containing 15 mg of N after 12 or 16 h ruminal preincubation (Goelema, 1999; Yu et al., 2003b) are exposed for 1 h in 10 mL of 0.1 N HCl solution containing 1 g L-1 of pepsin. The pH is neutralized with 0.5 mL of 1 N NaOH and 13.5 mL of pH 7.8 phosphate buffer containing 37.5 mg of pancreatin that are added to the solution and incubated at 38 °C for 24 h. After incubation, 3 mL of a 100% (wt/vol) trichloroacetic acid (TCA) solution are added to stop enzymatic activity and precipitate undigested proteins. Samples are centrifuged and the supernatant (soluble N) is analyzed for N (Kjeldahl method, AOAC 984.13). Intestinal digestion of protein is calculated as TCA-soluble N divided by the amount of N in the rumen residue sample (Calsamiglia, S. and Stern, M. D. 1995). Recently, 38 Peiqiang Yu researchers tried to use a Daisy II incubator with Ankom bags to estimate intestinal digestibility of feed rumen undegraded protein. However, our results with 10 different various co-products of bioethanol products showed significant and large differences (+8.5%) between the two methods (Original three-step method vs. Modified method: 87.4 vs. 78.9 %; n=10, P<0.05) (Data not shown, Azarfar and Yu)
2.5. In Situ Feed Degradation Kinetic Study
2.5.1. Rumen Incubation Procedure In a feed evaluation, we are also often to determine rumen degradation kinetics. The kinetics parameters are used for diet formulations. Feed rumen degradation parameters are normally determined using the in situ method described by Yu et al. (2000; 2003b) with as many fistulated dairy cow as possible. Briefly, seven (concentrate feed) or five (forage or roughage feed) grams of sample are weighed and placed into each numbered nylon bags (Nitex 03-41/31 monofilament open mesh fabric, Screentec Corp., Mississagua, ON) measuring 10 × 20 cm with a pore size of ca. 41 µm. The ratio of sample size to bag surface area was equal to 17.5 mg/cm2, which is within the range recommended by previous reports (Ørskov, 1982; Nocek, 1988; Nuez-Ortín and Yu, 2010.). A polyester mesh bag (45 cm × 45 cm with a ca. 90 cm length of rope to be anchored to the cannula) is usually used to hold the bags in the rumen. Sample bags are added into the polyester mesh bag according to the ‗graduate addition/all out‘ schedule and incubated for various times. The incubation times are dependent on the type of a feed. Normally a concentrate feed is incubated for 48, 24, 12, 8, 4, 2 and 0 h and a forage or roughage for 96 or 72, 48, 24, 12, 8, 4, 2 and 0 h. Published data are usually used to determine the number of bags incubated from each feed sample, which increased in relation to incubation time. This procedure gives us enough residual for further chemical analysis. For example, the multi-bags for each feed treatment at each incubation time in each experimental run are 2, 2, 3, 4, 5, 7 and 8 bags for incubation times 0, 2, 4, 8, 12, 24 and 48 h, respectively, in bioethanol co-products studies (Nuez-Ortín and Yu. 2010a). The maximum number of bags in the rumen at any one time is around 30. All feed treatments for each incubation time are incubated in two or three experimental runs and randomly allocated to the all available non-lactating cows (without knowing which bags go to which cows). We usually use 3 to 4 cows. After incubation, the bags are removed from the rumen and, together with those representing 0 h, rinsed under cold tap water to remove excess ruminal contents. The bags are washed with cool water without detergent. Normally we wash 10 bags per time in 10 liters of cold water container and wash for 6 times till water is clear, and then the bags are subsequently dried at 55°C for 48 h. Dry samples are stored in a refrigerated room (4°C) until analysis.
2.5.2. Rumen Degradation Kinetics Rumen degradation characteristics are determined for various nutrient components such as DM, OM, CP, starch, ADF and NDF. The percentage of each nutrient is fitted to the first- order kinetics equations described by Ørskov and McDonald (1979) and modified by Robinson et al. (1986) and Dhanoa (1988), Verite (1987), and Tamminga et al. (1994) to include lag time: R(t) = U + D × e -Kd x (t – T0), where, R(t) = residue present at t h incubation (%); U = undegradable fraction (%); D = potentially degradable fraction (%); T0 = lag time Animal Feeds and Nutrition Research 39
(h); and Kd = degradation rate (%/h). The results were calculated using the NLIN (nonlinear) procedure of SAS (2003) via iterative least squares regression (Gauss Newton method). Based on the nonlinear parameters estimated in the above equation (U, D, Kd), the effective degradability (ED), or extent of degradation, of each nutrient was predicted according to NRC (2001) as: ED (%) = S + D × Kd / (Kp + Kd), where, S = soluble fraction (%); Kp = estimated rate of outflow from rumen and is assumed to be 6%/h (Tamminga et al., 1994).
2.5.2.1. Modified Degradation Model of Non-Structural Carbohydrate (NSC) The rumen degradation of non-structural carbohydrate such as soluble sugars and starch is described well by assuming two fractions (S and D) in the modified first order kinetics degradation model (Tamminga et al., 1994), one soluble fraction of S which can be washed out without rumen incubation and the other one insoluble fraction of D which is degraded exponentially. The degradation model of NSC is:
R(t)=(100-S)*e-Kd*t (Tamminga et al., 1994), where, R(t) = residue of the amount of incubated material after t h of rumen incubation and assuming T0 = 0; U = 0.
Potentially degradable insoluble non-structural carbohydrate (PDNSC), calculated as:
PDNSC (g/kg DM) = NSC (g/kg DM) × D%, where, D% = 100-S%.
2.5.2.2. Modified Degradation Model of Protein The degradation model of protein is:
R(t) =U+(100-S-U)*e-Kd*(t-T0) (Verite et al., 1987; Tamminga et al. 1994), where, R(t) = residue (in %) of the amount of incubated material after t h of rumen incubation. Potentially degradable insoluble CP (PDCP), calculated as:
PDCP (g/kg DM) = CP (g/kg DM) × D%, where, D% = 100-S%-U%
2.6. In Vivo Feed Study
For a new/novel feed, we usually do two studies. The first study is a new feed palatability study. The other is a performance study. The following are examples of palatability and performance studies when we evaluated canola meal fractions-based two feeds as ruminant feeds (Heendeniya et al., 2010a; 2010b).
40 Peiqiang Yu
2.6.1. Palatability Study Palatability is a major concern when it comes to feeding a non-conventional ingredient (Heendeniya et al., 2010a). Palatability of a feedstuff is influenced by its oropharyngeal stimulants such as taste, odour and texture (Kaitho et al., 1997). In our study on canola meal fractions designed as animal feed, palatability difference between two test feeds (Heendeniya et al., 2010a) was evaluated by ―Two choice alternating access method‖ (Paterson, 1996) using six multiparous Holstein cows (body weight 737 ± 46 kg; days in milk 127 ± 36; milk yield 42 ± 5 kg). Here is brief procedure reported by Heendeniya et al. (2010a). During an adaptation period of 8 days, the two test feeds were given as top dressing to TMR on alternative days, starting from 0.5 kg on the first two days and gradually increasing to 2 kg by 7th/8th day. Following the preliminary period, palatability was measured for 7 days. Two test feeds were offered to animals one at a time in blue color tubs and exchanged the tubs at 5 minutes intervals, which continued for a maximum period of 30 minutes in the morning (0800 h) and afternoon (1600 h) just before feeding the basal diet. Basal diet along with test feeds was balanced to meet the nutrient requirements as per NRC (2001) recommendations for lactating dairy cows (3 × time maintenance). At a time, 0.5 kg of each test feed was placed in front of each animal in tubs 4 times per day thereby total 2 kg of both test feeds was offered per day per animal. The type of feed that was offered first was also alternated between morning and afternoon as well as between consecutive days to eliminate possible bias in a pattern of offering. At the end of 30 minutes, the remaining test feed in the tubs was measured. Eating time was recorded for each animal if an animal stopped eating or finished the feed in a tub. The morning and afternoon intakes were totalled to find the daily intake of test feed by each cow and preference percentage was calculated as:
Intake Pellet A Preference % = Intake Pellet A Intake Pellet B × 100
2.6.2. Animal Performances Study The following is an example of our animal performance study on a new feed reported by Heendeniya et al. (2010a). In the animal performance study on canola meal fractions designed as animal feeds (Heendeniya et al., 2010a), six multiparous Holstein cows (body weight 760 ± 55 kg; days in milk 155 ± 36) were used in this animal trial. The experimental design was a switchback/crossover that included two animal groups and three experimental periods. Animals were randomly assigned into the two groups. Each experimental period was 21 days long and consisted of 6 days adaptation period followed by 15 days measurement period. Feed was offered twice a day at 0800 and 1600 h. Test feed pellets (Heendeniya et al., 2010a; 2010b) were mixed manually to the basal diet at the rate of 1 kg (dehydrated-pellet) per 21 kg (basal diet) (as fed basis). The ingredient and nutrient composition of the total rations (TMR), balanced to meet the nutrient requirements of lactating dairy cows as per NRC 2001 recommendations. The daily intake of each animal was recorded during the 15 days measurement period and closely monitored to prevent both under feeding. Feed samples were collected on every other day to obtain cumulative samples of basal diet and two test feeds during the last 10 days of each experimental period. Feed samples were dried in a forced air oven set at 55oC for 48 Animal Feeds and Nutrition Research 41 hours to obtain the DM content and calculate dry matter intake (DMI) (Heendeniya et al., 2010a). Fecal samples were drawn from each animal at 1930 h during the last three days of each experimental period and dried in a forced air oven set at 55oC for 72 hours. Equal amounts of dried fecal samples were pooled together to obtain 3-days-cumulative samples for each animal during each period. Milking was done twice a day at 0600 and 1600 h. Individual milk yields were recorded during the last 10 days of each experimental period. Two milk samples were drawn at the end of milking from each animal for 3 consecutive days on the last Monday, Tuesday and Wednesday of each test period. One set of milk samples was frozen (at -20oC) immediately after milking and the other set was refrigerated after adding a preservative tablet (Brotab ―10‖ containing 7.83 mg 2-Bromo-2-Nitropropane-1,3 Diol and 0.35 mg Pimaricin, DandF Control Systems Inc., Dublin, CA, USA). Morning and afternoon milk samples on each day were pooled together, in quantities proportionate to morning and afternoon milk yields of each animal. The milk samples with the preservative were tested for milk fat, milk protein and lactose while frozen samples were analysed for milk urea (MU). Milk sample analysis with a NIR method was conducted at the Saskatchewan Agriculture Provincial Dairy Laboratory, 4840 Wascana Parkway, Regina, Saskatchewan, Canada (Heendeniya et al., 2010a).
2.7. Two Degradation Ratio Systems to Indicate Rumen N to Energy Synchronization
2.7.1. Hourly Effective Degradation Ratio Animal performance is related to the truly absorbed protein in the small intestine, which is largely determined by microbial protein synthesized in the rumen and rumen undegraded protein (Nuez-Ortín and Yu, 2010a). In order to achieve optimum microbial protein synthesis, the degradation of protein in rumen should match that of OM, particularly the carbohydrate fraction. Thus, the ratios between the effective degradability of N and OM should be used in feed formulation to optimize the composition of dairy, beef and sheep diets (Tamminga et al., 1990; 1994). The effective extent of degradation of N and OM is calculated hourly as outlined by Sinclair et al. (1993) as:
Hourly ED (g/kg DM) = S + [(D × Kd) / (Kp + Kd)] × [1 – exp – t × (Kd + Kp)].
The difference in cumulative amounts degraded between successive hours is regarded as the quantity degraded per hour. From the quantity of N and OM degraded per hour, an hourly ratio of N to OM is calculated:
Hourly ED ratio N/OMt = (HEDNt – HEDNt-1) / (HEDOMt – HEDOMt-1), where, ratio N/OMt = ratio of N to OM at time t (g N /kg OM); HEDNt = hourly effective degradability of N at time t (g/kg DM); HEDNt-1 = hourly effective degradability of N 1 h before than t (g/kg DM); HEDOMt = hourly effective degradability of OM at time t (g/kg DM); and HEDOMt-1 = hourly effective degradability of OM at 1 h before than t (g/kg DM). 42 Peiqiang Yu
As reported by Czerkawski (1986) and Tamminga et al. (1990, 1994), 25 g N /kg OM truly digested in the rumen is the optimal ratio to maximize microbial protein synthesis efficiency.
2.7.2. Tamminga’s Degradation Ratio System A knowledge of rumen availability of each component is important because microbial protein synthesis in the rumen is highly related to the availability of each feed component in the rumen (Nuez-Ortín and Yu, 2009). In model feed formulation, the degradation ratios can be used in assisting to optimize the composition of dairy diets (Tamminga et al., 1990; 1994). The ratios of rumen available carbohydrate to available protein and the degradation of carbohydrate to protein should be optimum and synchronized to achieve efficient microbial growth and minimize N loss in the rumen. This can be expressed in part by FN/FCHO, EN/ECHO and SN/SCHO ratios (Tamminga et al. 1990). The abbreviations we used here are originally from the degradation ratio system reported by Tamminga et al. (1990). The rumen degradation characteristic ratios can be used to formulate diets that meet the requirements for optimum rumen fermentation. Based on the measured characteristics, the rumen degradation characteristic ratios are calculated by the following the formulas from Tamminga et al. (1990) and summarized by Yu (2008):
EN/ECHO (g/kg) = Insoluble rumen available N / Insoluble rumen available carbohydrate SN/SCHO (g/kg) = Soluble rumen N / Soluble rumen carbohydrates FN/FCHO (g/kg) = Total rumen available N / Total rumen available carbohydrates
2.8. Modeling Nutrition Supply from a Feed to Animals
For a new/novel feed, we can use available feed evaluation systems to predict/model nutrient supply to animals. Here I list two advanced systems here which we often use to evaluate conventional and non-conventional feeds. For examples, when we evaluated the effect of bioethonal plants and co-products type on metabolic characteristics of protein, we used the following two systems (Nuez-Ortín and Yu, 2010a, b)
2.8.1. Non-TDN Based System: DVE/OEB System The detailed concepts and formulas of the DVE/OEB system are provided by Tamminga et al (1994). The following is a brief explanation in order to understand how to predict nutrient supply from a feed to the small intestine of dairy cows.
2.8.1.1. Calculation of FOM and RUPDEV Ruminally undegraded feed protein (RUPDEV) is calculated as: RUPDEV = 1.11 CP RUP, where, RUP = U + D Kp / (Kp + Kd), the passage rate (Kp) of 0.06/h is adopted (Tamminga et al., 1994). The content of organic matter fermented in the rumen (FOM) is estimated from digestible organic matter subtracted ether extract (EE), RUPDEV, ruminally undegraded feed starch and fermentation products.
Animal Feeds and Nutrition Research 43
2.8.1.2. Microbial Protein Synthesis in the Rumen
Microbial protein synthesized in the rumen (MCPFOM) is estimated as 15% of the rumen fermented organic matter (FOM). Of the microbial protein, 75% is added to the undegraded feed protein (RUPDEV) to estimate the true protein supplied to the small intestine (TPSI). The remaining 25% represents N in nucleic acids.
2.8.1.3. Intestinal Digestion of Feed and Microbial Protein The previously discussed RUPDEV and TPSI must be corrected for incomplete digestion and endogenous secretions.1 A correction is needed for protein losses due to incomplete digestion and from endogenous secretions. True digestibility of microbial protein is assumed to be 85% and therefore the amount of truly absorbed rumen synthesized microbial protein in DVE DVE the small intestine (AMCP ) is estimated as: AMCP = 0.85 0.75 MCPFOM. For feed ingredients, the content of truly absorbed bypass feed protein in the small intestine (ARUPDVE) is calculated as: ARUPDVE = dRUP RUPDVE.
2.8.1.4. Endogenous Protein Losses in the Small Intestine Endogenous protein losses in the digestive tract (ENDP) are related to the amount of undigested DM (UDM) excreted in the faeces, calculated as: ENDP = 0.075 UDM, where, UDM is calculated as undigested organic matter (UOM) plus undigested ash (UASH).
2.8.1.5. Truly Digested and Absorbed Protein in the Small Intestine Truly digested and absorbed protein in the small intestine (DVE value) are contributed by 1) feed protein escaping rumen degradation (RUPDVE), 2) microbial protein synthesized in the
rumen (MCPFOM), and 3) a correction for endogenous protein losses in the digestive tract (ENDP). Therefore the DVE value is estimated as: DVE = ARUPDVE + AMCPDVE - ENDP.
2.8.1.6. Degraded Protein Balance The degraded protein balance (DPBOEB) is the balance between microbial protein synthesis from rumen degradable protein and that from the energy extracted during anaerobic OEB DVE fermentation in the rumen. Therefore the DPB value is estimated as: MCPRDP - DVE MCPFOM, where, MCPRDP = CP - 1.11 RUP.
2.8.2. TDN-Based System: NRC-2001 Model The detailed concepts and formulas are provided by NRC (2001). A brief explanation is as follows:
2.8.2.1. Calculation of RDPNRC and RUPNRC Ruminally undegraded feed protein is calculated as: RUPNRC = CP RUP. Ruminally degraded feed protein is calculated as: RDPNRC = CP RDP, where, RDP is calculated as: RDP = S + D Kd / (Kp + Kd), where, Kp of 6%/h is adopted for concentrate feedstuff. Kp of 4%/h is adopted for forage or roughage feedstuffs.
44 Peiqiang Yu
2.8.2.2. Rumen Microbial Protein Synthesis Ruminally synthesized microbial protein is calculated as: MCPNRC = 0.13 × TDN, when NRC NRC RDP exceeded 1.18 × TDN-predicted MCP (MCPTDN). When RDP is less than 1.18 × NRC NRC TDN-predicted MCP (MCPTDN), then MCP is calculated as 0.85 of RDP (MCPRDP).
2.8.2.3. Intestinal Digestion of Feed and Microbial Protein Digestibility and true protein of ruminally synthesized microbial protein are assumed to be 80%, therefore the amount of truly absorbed MCPNRC is estimated as: AMCPNRC = 0.80 0.80 MCPNRC. Truly absorbed rumen undegraded feed protein in the small intestine (ARUPNRC) is calculated as: ARUPNRC = dRUP RUPNRC, where dRUP is digestibility of RUP.
2.8.2.4. Rumen Endogenous Protein in the Small Intestine Rumen endogenous CP is calculated as: ECP = 6.25 1.9 DM/1000 (where, DM in g/kg) Assuming that 50% of rumen endogenous CP passes to the duodenum and 80% of rumen endogenous CP is true protein, therefore the truly absorbed endogenous protein in the small intestine (AECP) is estimated as: AECP = 0.50 0.80 ECP.
2.8.2.5. Total Metabolizable Protein Metabolizable protein is contributed by 1) digestible RUPNRC, 2) digestible MCPNRC, and 3) ECP, calculated as: MP = ARUPNRC + AMCPNRC + AECP.
2.8.2.6. Degraded Protein Balance The degraded protein balance (DPBNRC) reflects the difference between the potential microbial protein synthesis based on ruminally degraded feed crude protein (RDPNRC) and that based on 1.18 times of energy (TDN) available for microbial fermentation in the rumen, NRC NRC calculated as: DPB = RDP - 1.18MCPTDN.
2.9. A Novel Approach: Application of DRIFT Molecular Spectroscopy in Feed Structure Research
The Diffused Reflectance Infrared Fourier Transform spectroscopy (DRIFT) has been developed as a rapid and direct bioanalytical technique. This technique is capable of exploring the molecular chemistry and biopolymer conformation through molecular and functional group spectral analyses. To date there has been a few applications of the DRIFT technique plus multivariate molecular spectral analyses (Doiron et al., 2009a; Damiran and Yu. 2010a; Liu and Yu, 2010a) to the study of feed molecular structure. The detailed methodology is as follow: For DRIFT molecular structural study (Yu et al., 2010a), feed samples are usually finely ground and then mixed with potassium bromide (KBr, IR grade, P5510, Sigma) in a ratio of 1 part of sample to 4 parts of KBr in a 2 ml centrifuge tube and mixed by vortex for minutes. The DRIFT is performed in a diffuse reflection mode using a Bio-Rad FTS-40 with a Ceramic IR source and MCT detector (Bio-Rad laboratories, Hercules, CA, USA) at the Saskatchewan Structural Sciences Center (SSSC), University of Saskatchewan, Saskatoon, Animal Feeds and Nutrition Research 45
Canada. The data are collected using Win-IR software (Bio-Rad Digilab, Cambridge, USA). Spectra are generated from the 4000-500 cm-1 portion of the electromagnetic (EM) spectrum with 256 co-added scans and a spectral resolution of 4 cm-1. Spectral analysis is usually done with OMNIC 7.3 (Spectra Tech, Madison, WI) software. Multivariate molecular spectral analyses, principal component analysis (PCA) and hierarchical cluster analysis (CLA), are usually performed using Statistica software 6.0 (StatSoft Inc, Tulsa, OK, USA) to classify and distinguish between feeds and feed inherent structures (Yu et al., 2010a). The detailed concepts of PCA and CLA molecular spectral analyses in feed study have been reported (Yu, 2005a). This DRIFT technique plus multi-peak modeling technique (Yu, 2005b) have been applied to study chemical functional group features in various types of ground feeds such as protein secondary structure (Yu, 2005c ) and asymmetric and symmetric CH2 and CH3 groups and their ratio of biopolymers within intact tissue in complex plant system (Yu, 2010a)
2.10. A Novel Approach: Application of Synchrotron-Based Infrared Microspectroscopy in Feed Research
Advanced synchrotron radiation based FTIR microsepctroscopy (SRFTIRM), taking advantage of bright synchrotron light (million times brighter than sunlight), is capable to detect structure information at both cellular and molecular levels within intact feed tissues (Yu, 2004). Unlike traditional ―wet‖ chemical analysis who fails to detect feed intrinsic structures and matrix during the analysis. The SRFTIRM can provide four kinds of information simultaneously: feed tissue chemistry, feed tissue composition, feed tissue environment, and feed tissue structure (Budevska, 2002). The detailed advantages, applications of the SRFTIRM, why ―wet‖ chemical analysis fails to detect feed structural feature, and why needs SRFTIRM for feed inherent structure research have been reported in British Journal of Nutrition (Yu, 2004). The synchrotron radiation based bioanalytical technique can be used to increase the fundamental understanding of feed structures at the cellular and molecular levels in relation to nutrient availability (Yu et al., 2004a-d); Doiron et al., 2009b; Liu and Yu. 2010b). With the SFTIRM technique, we are able to image feed molecular chemistry (Yu et al., 2004a; Yu et al., 2007) and study biopolymer conformations (Yu, 2006; 2010a). It brings a new level of understanding of feed analytical information (Budevska, 2002; Yu, 2004). This technique has been applied to study the impact of gene- transformation, heating treatment and bioethanol processing on feeds (Yu, 2010b).
3. CONVENTIONAL AND NON-CONVENTIONAL FEED RESEARCH PROGRAMS
3.1. Effect of Foreign Gene Transformation on Forage Feed Nutritive Value
Forages such as sainfoin are considered bloat-safe due to the presence of proanthocyanidins (PA) (Wang et al., 2006a,b). PAs interact with plant protein to form PA- protein complexes during mastication (chewing and rumination). These complexes prevent 46 Peiqiang Yu plant protein from being solubilized into ruminal fluid. Consequently, formation of the protein foam is inhibited or disrupted when PAs are present and bloat and acidosis is prevented. Moreover, PAs improve protein utilization, reduce greenhouse gas production, and reduce intestinal parasite load in ruminants. Grazing on mixed alfalfa pastures that contain PA-accumulating forage species is a method of reducing bloat (Wang et al., 2006a,b). However, the efficacy of bloat reduction in mixed pastures is contingent upon the ratio and accessibility of the PA-containing forage in the total herbage mass. Moreover, competition between forages is an important factor that limits the persistence of a mixed pasture (Wang et al., 2006a,b), and the genetic variability and adaptability of many of the PA-containing forages is very limited (Wang et al., 2006a,b). Therefore, it would be of great benefit to livestock producers if a new genotype of alfalfa that produced moderate amounts of PA became available in Saskatchewan (Jonker et al., 2010a,b). Recently, Dr. Margaret Gruber‘s research team at the Saskatoon Research Centre has successfully developed four new genotypes of alfalfa in which moderate amounts of anthocyanidin (Anth) and small amounts of proanthocyanidin (PA) accumulate in forage. This was accomplished through modern molecular genetic techniques (Ray et al., 2003). Normally, alfalfa accumulates these compounds in the seed coat, but not in the forage. This new Anth+PA+ alfalfa is platform germplasm, which is being used in combination with other novel genes to produce advanced elite germplasm with moderate amounts of PA in the forage (called ‗TANNIN ALFALFA‘). Information correlating the level of anthocyanidin and proathocyanidin with nutrient utilization and availability in ruminants particularly within the specific nutrient fractions required for modern diet formulation programs, is not well established for alfalfa. This information is critical to obtain using PA and anthocyanidin metabolites developed within alfalfa, rather than using non-alfalfa components mixed with alfalfa. PA interactions (and consequently digestion) differ with different types of PA, protein, and carbohydrate structure. Earlier experiments on rumen digestibility and PA used non-alfalfa components and were limited in their component analysis. Therefore, there is an urgent need to evaluate this new material in support of further improvements to alfalfa that are underway, on behalf of the Saskatchewan forage, livestock and feed formulation industries, and in anticipation of export and marketing requirements and the development of low-cost feeding programs. This research program aims to develop a super-genotype of alfalfa for the forage, livestock, and feed industries. This new super-genotype, which will accumulate optimized levels of leaf proanthocyanidin, will significantly improve alfalfa utilization efficiency and quality, reduce incidents of bloat and acidosis, reduce environmental impact, and maintain animal health. Information arising from the analysis of intermediate platform germplasm, as well as the final ‗TANNIN ALFALFA‘ germplasm, will be applied to the production of high quality beef and dairy feeding programs and also to support the alfalfa export industry. Currently, Jonker et al. (2010a,b) are investigating these transgenic Lca. Alfalfa. The nutrient composition and degradation profiles of anthocyanidin-accumulating Lc-alfalfa populations have been published (Jonker et al., 2010a). The modeling degradation ratios and nutrient availability of anthocyanidin-accumulating Lc-alfalfa populations have been studied in dairy cows (Jonker et al., 2010b).
Animal Feeds and Nutrition Research 47
3.2. “Double-Low” Feed Barley Variety Research: Development of a „Double Low” Feed Barley with Low Hull Content and Low Content of Hydroxycinnamic Acids in the Hulls for Ruminants
An understanding of relationships between complex plant cell wall constituents and cell wall rumen degradation and digestion is important for efficient animal production (Yu et al., 2005a). A great deal of research has been expended on the identification and examination of factors affecting complex cell wall (fiber) fermentation by ruminants (eg. Yu et al., 2004e, 2005a; Du et al., 2009). This research has focused on factors such as core lignin composition (Yu et al., 2008a, 2010b) and concentration, lignin-carbohydrate and phenolic-carbohydrate complexes (Yu et al., 2002a,b), lignin encrustation, hemicellulose encrustation and cellulose crystallinity (Van Soest, 1975; Gordon et al., 1977; Jung et al., 1983; Sawai et al., 1983; Garleb et al., 1988). Barley hulls, a relatively indigestible portion of the barley grain, are the main reason for its lower energy relative to corn. Nutritionally, barley hulls are high in fiber and low in protein. Due to the large supply of barley as a feed, it is economically important to improve the nutritional qualities of barley hulls (Du et al., 2009). Barley hulls contain hydroxycinnamic acids, mainly ferulic acid (3-methoxy-4-hydroxy-cinnamic acid) and para- coumaric acid (4-hydroxycinnamic acid) (Bartolomé et al., 1997a,b; Bartolomé and Gómez- Cordovées, 1999; Du et al., 2009; 2010a). These hydroxycinnamic acids produced via the phenylpropanoid biosynthetic pathway (Faulds and Williamson, 1991, 1999; Faulds et al., 1995; Kroon et al., 1996) and are covalently cross-linked to polysaccharides by ester bonds and to components of lignin mainly by ether bonds (Brézillon et al., 1996; Bartolomé et al., 1997a,b; Faulds and Williamson, 1999). These cross-links are a barrier to biodegradation and limit cell-wall degradability by rumen microorganisms. It is believed that these hydroxycinamic acids are the factor most inhibitory to the biodegradability of plant cell wall polysaccharides (Borneman et al., 1990; Yu et al., 2005a). This feed research program aims to determine genotypic differences in chemical composition, total content of hydroxycinnamic acids, rumen degradation kinetics of hydroxycinnamic acids and structural-chemical features among various varieties of barley and barley hulls (Du et al., 2009; 2010a,b). Total hull content of barley varieties are measured to identify differences in barley hulls content. The final goal of this feed research program is to select a superior variety of barley (we called ―DOUBLE LOW BARLEY‖: low hull content and low content of hydroxycinnamic acids in the hulls) with a high nutritive value to develop a complete feed for ruminants for the Saskatchewan Feed and Livestock Industry. The hypothesis of this research program is that the hydroxycinnamic acids in the complex cell walls of barley hulls are associated with nutritive values and nutrient utilization and availability. Barley hulls may significantly differ in the content of hydroxycinnamic acids, and degradation kinetics of hydroxycinnamic acids among various varieties and may significantly differ in structural-chemical features. Such differences influence hull quality, digestive behaviour, nutrient utilization and availability. Lower hydroxycinnamic acid in barley hulls may result in higher nutritive value for ruminants. Similarly, total hull content is related to dry matter digestibility and nutrient availability and varieties with low hull content should be superior even if hydroxycinnamic acid content is consistent. The superior variety of barley is, ―double low barley‖, with low hull content and low content of hydroxycinnamic acids in the hulls. 48 Peiqiang Yu
3.3. Effect of Heating Processing on Nutrient Availability of Feeds
In the study, Yu (2005c) indicated that heat processing (eg. pressure toasting, extrusion, micronization, pelleting, autoclaving, roasting) has been used to improve utilization and availability of the nutrients (Yu et al. 2002c) and inactivate anti-nutrition factors (ANF) (van der Poel et al. 1990) in feeds, such as reducing the solubility of the protein (Yu et al., 2000), reducing fermentation and metabolism in the rumen, increasing the amounts of protein entering the small intestine for absorption and digestion (Yu et al. 2002c), and reducing conjugated linoleic acid (CLA) hydrogenation in the rumen and increasing the amount of CLA available in the small intestine (Soita et al., 2003). The basic mechanism of altering the protein digestive behavior (Goelema, 1999) with heat processing involves denaturation, unfolding or uncoiling of a coiled or pleated structure (Holum 1982). Any temperature change in the environment of the protein which can influence the non-covalent interactions involved in the structure may lead to an alteration of the protein structure (Goelema, 1999) including protein amide I to II ratio and protein secondary structures (Yu, 2005c. Doiron et al. 2009a,b; Yu et al., 2010a). Yu (2005c) indicated that the effects of heat processing on protein nutritive value, utilization and availability and performance in animals are very equivocal. Part of reason is that heating conditions of inside a feed may not be optimal, the feed being either underheated or overheated. Most studies have focused on total chemical composition affected by heat processing using traditional ―wet‖ chemical analysis without consideration of any inherent structural and matrix effects (Yu, 2004). As we know, the protein value, quality, utilization and availability and digestive behavior are closely related to not only total chemical composition, but also inherent structures and component matrix (Yu et al., 2004a; 2008b). To date there has been very little application of the novel synchrotron-based bioanalytical technique-SFTIR (Yu, 2004) to the study of inherent structures of feeds in relation to nutrient utilization, digestive behaviors and availability in animals (Yu, 2004). No study has been carried out to study the ultra-structural chemical and nutritive features of protein secondary structures in a feed within intact ―pure‖ protein tissue at a cellular level in relation to feed protein quality, utilization and availability in animals. Our feed heating processing research program (Doiron et al., 2009; Yu and Nuez-Ortín, 2010) aims to use the advanced synchrotron technology (SFTIR) as a novel approach to reveal molecular chemistry of protein secondary structures of feed tissues affected by heat processing within intact tissue at a cellular level, and quantify protein secondary structures using multi-component peak modeling Gaussian and Lorentzian methods, in relation to protein digestive behaviors and nutritive value in the rumen. Information from the study by the infrared probing of feed protein secondary structures will be valuable as a guide to maintain protein quality. It is believed that by using the advanced synchrotron technology (SFTIR), it will make a significant step and an important contribution to protein nutritional research (Yu and Nuez- Ortín, 2010).
Animal Feeds and Nutrition Research 49
3.4. Super Genotype of Feed Oats with Low Lignin and High Fats
3.4.1. Protein Value of a Super Genotype of Oat (CDC SO-I) for the NRC-Dairy Model Recently, a new genotype of oat has been developed in Crop Development Center, called CDC SO-I (―SuperOat‖) and showed promise for oat use in ruminant rations. The input costs are relatively lower and the yield is relatively higher as compared to barley grain. This new genotype of oat contains low-lignin hull and high-fat groat (Yu et al., 2008a), thus is expected to be a superior oat for feeding dairy cattle. However, no quantitative evaluation of protein supply from the CDC SO-I to dairy cow has been investigated in terms of potential protein degradation balance (PDB) and total metabolizable protein (MP). These data are crucial in order to develop more efficient, competitive and optimal feeding the new genotype of oat (CDC SO-I) for livestock. This project (Yu et al., 2008a) aims to use the NRC model (2001) with inputs based on laboratory and in situ animal studies to predict the potential nutrient supply to dairy cows from the CDC SO-I in comparison with two conventional oat varieties- CDC Dancer and Derby in western Canada. The quantitative predictions are made in terms of: 1) Rumen synthesized microbial protein truly absorbed in the small intestine (AMCP); 2) Rumen undegraded feed protein truly absorbed in the small intestine (ARUP); 3) Endogenous protein in the digestive tract (AECP); 4) Total metabolizable protein supply in the small intestine (MP), and 5) Protein degraded balance (Yu et al., 2008a). The detailed modeling nutrient supply to dairy cattle from the super oats has been reported in Yu et al. (2008a).
3.4.2. Effects of Partially Replacing Barley or Corn with Raw and Micronized CDC SO-I Oats on Productive Performance of Lactating Dairy Cows Cereal grains, such as wheat, barley and corn, are the most common dairy concentrates and on DM basis, rations for high producing herds consist up to 60% of cereals (Yu et al., 2010b). Traditionally, in western Canada barley is a dominant cereal grain fed to dairy herds as a concentrate supplement, but there may be advantages in utilizing a locally available alternative grain such as oats (Yu et al., 2010b). Usually, conventional oats have a lower energy value for dairy cows than other grains (Morgan, 1986). Previous studies of conventional oats inclusion in dairy rations showed that, comparing to wheat, barley and corn, feeding oats may have some impacts on milk yield, milk fat and milk composition (Yu et al., 2010b). A new genotype of oat variety (cv. CDC SO-I), developed at the Crop Development Centre (University of Saskatchewan, Canada), originated from the cross of AC Assiniboia and SA96121. Selection criteria for SO-I included high yield potential, early maturity, straw strength, disease resistance (B. Rossnagel, personal information), and grain quality including the combination of low-lignin hull and high-fat groat characteristics (Damiran and Yu, 2010a). Comparing oats with barley, the harvested area for oats was lower than barely (769,000 vs. 1,457,000 ha in 2008), thus the production of oats was lower than barely (2.27 · 106 t vs. 4.59 · 106 t in 2008), the yield of oats were close to barley (2.95 vs. 3.15 t/ha in 2008) and the price of oats was lower than that of barley (CA$ 102 vs. 115 per tonne in February 2009) (Saskatchewan Government-Agriculture, 2009). Previous studies (Fuhr, 2006; Yu et al. 2008a) showed that the newly developed CDC SO-I oats contains lower acid detergent lignin (ADL: 2.12 vs. 3.75 g/kg DM) and higher crude fat (CF: 5.85 vs. 4.29 g/kg DM), and could 50 Peiqiang Yu be a good energy source for dairy cows. Yu et al. (2008a) showed that the CDC SO-I oats has a very high soluble crude protein content (SCP, 53 % CP), in situ rumen protein content (82%) and starch degradation (89%), resulting in imbalance between oats breakdown and microbial synthesis and inefficient nutrient utilization of oats for dairy cows. Micronization is a type of heat treatment that can be used to decrease SCP and rumen protein and starch degradation, thus resulting improved dairy performance. This super-oat feed project aims to determine the effects of partially replacing barley or corn by CDC SO-I oats and micronization of CDC SO-I oats on productive performance of lactating dairy cows. The detailed results from our dairy cow performance study will be published soon (Yu et al., 2010b)
3.5. Fractionation of Canola Meals as Ruminant Feeds
3.5.1 Nutritional Evaluation of Canola Meal Protein Fractionation By-Products for Ruminants In this feed study, Heendeniya et al. (2010b) indicated research background and motivation that recently there have been studies to develop processing technologies to fractionate oil extracted canola meal with the intention of developing feed products for specific feed markets, including a cost-effective alternative for animal-based protein such as fish meal (Thiessen et al., 2004). In one such study, the meal portion of canola was fractionated to extract protein concentrates. In this process, in addition to the protein concentrates, two other products are fractionated. These are ―fibre-protein‖ and ―can-sugar‖ fractions. The fibre-protein contains mainly hulls with some dockage that was present in the raw canola meal, while can-sugar contains mainly the water soluble non protein components of canola meal (Heendeniya et al., 2010b). In order to obtain the maximum advantage from a commercially operated canola protein fractionation process, it is important to utilize both fibre-protein and can-sugar. The CP content in fibre-protein and can-sugar was reported to be 310 g/kg and 170 g/kg, respectively. However, in view of the presence of high fibre level (NDF 556 g/kg DM) in fibre-protein and high ash content (210 g/kg DM) in can-sugar, the value of these ingredients in monogastric diets would be limited and the most likely market would be as dietary ingredients for ruminants. However, there is very little information available on these by- products, particularly as they relate to the nutritional needs of ruminant animals (Heendeniya et al., 2010a,b). The aims of this feed project are to evaluate fibre-protein and can-sugar as feed ingredients in ruminant diets. This is achieved by examining (1) chemical profiles, (2) rumen degradation characteristics, (3) nutritive value, and (4) modeling nutrient supply of fibre-protein and can-sugar fractions as dietary components for dairy cattle in comparison with commercial canola meal and soybean meal. The detailed results from our study are reported in Heendeniya et al. (2010b).
Animal Feeds and Nutrition Research 51
3.5.2. Utilization of Canola Seed Fractions for Ruminants: Effect of Canola Fibre- Protein and Can-Sugar Inclusion in Dehydrated Alfalfa Pellets on Palatability and Lactation Performance of Dairy Cows In this study, Heendeniya et al. (2010a) indicated that canola is the second most economically important crop grown in Canada. About 67% of canola meal is exported by Canada (Canola Council of Canada, 2009). Due to high level of fibre (NDF~300 g/kg) and phytate (31 g/kg), canola meal has limited use in aquaculture, swine and poultry feeding, thereby fetching a lower price compared to soybean meal. The production of canola is expected to increase substantially in Canada within the next 10 years, particularly to meet the envisaged bio-diesel demand of 600 million litres per annum by year 2012. In order to maximise return from canola, it is necessary to add value to canola meal. Recently, there were attempts to develop a new processing technology to extract high quality protein from canola meal. Canola ―fibre-protein‖ and ―can-sugar‖ are the two by-products arising from one such method of canola meal fractionation (Heendeniya et al., 2010a,b). As indicted by Heendeniya et al. (2010a), palatability is a major concern when it comes to feeding a non-conventional ingredient. Palatability of a feedstuff is influenced by its oropharyngeal stimulants such as taste, odour and texture (Kaitho et al., 1997). Fibre-protein is high in canola hulls and other fibrous material found in canola meal and does not possess a detectable odour. Can-sugar is available as a highly hygroscopic powder consisting of water soluble fractions (i.e. sugars, nitrogenous compounds and minerals). In view of the possible low palatability of fibre-protein if fed alone due to its physical characteristics, it was decided to incorporate a combination of fibre-protein and can-sugar into alfalfa dehydrate pellets, since the combined chemical composition of fibre-protein and can-sugar is close to the chemical composition of dehydrated alfalfa (Heendeniya et al., 2010a). In order to evaluate the potential of utilizing fibre-protein and can-sugar mixture as an additive to alfalfa pellets used in dairy cattle rations, two studies were conducted. The project aims are to determine the effect of fibre-protein and can-sugar fractions used as additives in dehydrated alfalfa pellets on feed acceptance by dairy cows and to investigate the effect of fibre-protein and can-sugar blended alfalfa pellets on lactation performance, dry matter intake and apparent dry matter digestibility of dairy cows, compared with a standard (pure) alfalfa pellet (Heendeniya et al., 2010a). The detailed results from our study have been published in Heendeniya et al. (2010a).
3.6. Molecular Imaging of Nutrient and Nutrient Ratio in Feeds
With advanced synchrotron-based analytical technology -SFTIRM, we are able to image nutrient distribution and intensity, biological component and biopolymer within intact feed tissues at cellular and subcellular levels (Yu et al., 2004b). This makes it possible to rapidly characterize feed inherent structure. The photomicrographs of the feed tissues, even including electronic micrograph, are not very informative with respect to molecular chemical make-up and structural-chemical features of the inherent structures at ultraspatial resolution. With the synchrotron-based technology, the structural-chemical information of various feeds can be revealed by imaging or mapping. We have done molecular chemistry imaging in several 52 Peiqiang Yu cereal grain seeds (eg. barley, wheat, corn, sorghum) (Yu et al., 2007) and forage seed (eg. winterfat) (Yu et al., 2005b).
3.7. Nutrient Variation and Availability of New Co-products from BioEthanol Processing
The increasing demand for ethanol has been addressed in Western Canada with the high use of wheat as a feedstock for ethanol production (Nuez Ortín and Yu, 2009). In addition, the poor social acceptance of products originating from genetically modified corn makes wheat DDGS a potentially exportable feedstuff. On the other hand, the fluctuation in the price of wheat has forced ethanol companies to include corn in the feedstock for ethanol processing. As a result of this, there has been an augmented availability of both pure wheat DDGS and wheat/corn blend DDGS. Thus, the livestock industry in Canada and other outside markets want to know the detailed nutritional value of these by-products. Concentration for most nutrients in DDGS is higher than in original feedstock due to starch removal (Nuez Ortín and Yu, 2009). Reduced starch intake increases the consumption of digestible fibre, and helps to reduce or prevent the occurrence of subacute acidosis in ruminant animals. Besides being a good protein source in growing and finishing diets, some studies suggest that corn DDGS has also proven to have greater energy for growth than dry rolled corn. In dairy cattle, corn DDGS provided with significant amounts of rumen undegradable protein besides being described as a high quality protein source. The market value is affected by the nutritional properties and by the inconsistency of DDGS, which may lead to misformulation of the ration and diminished animal productivity. There has been reported plant to plant and batch to batch variability that has been attributed to different processing techniques such as the fermentation conditions, drying method, amount of solubles added back, type of grain used or grinding procedure. By detecting plant to plant differences in the characterization of the final product, sources of variation can be identified and subsequent improvements in the processing techniques can be developed. As a result, product quality and consistency can be achieved, more precise diet formulations are created, and the stability of the ethanol plant is beneficially maintained. As the price of cereal grains continues to increase, the demand for alternative feed ingredients such as DDGS which provide sources of energy and protein in livestock diets will also increase. While the nutritional value of corn DDGS has been extensively documented in ruminants, information on wheat DDGS and wheat/corn blend DDGS has only been reported in pigs. Thus, a database on chemical profile, protein and carbohydrate fractions, and energy values needs to be created in order to proceed with feeding recommendations for ruminants. Likewise, it is important to detect plant to plant variations and how this product inconsistency affects nutrient supply and consequently animal performance in western Canada. To produce top quality DDGS suitable for export and marketing, and for livestock industry within Canada, there is an urgent need for this information. So far, little research has been conducted to determine the magnitude of the differences in nutritive value among wheat DDGS, corn DDGS and blend DDGS (particular blend DDGS), between bioethanol plants in Canada. This project aims are to compare different types of new co-products DDGS from different bioethanol plants and compare from original feedstocks in Canada in nutrient variation and availability of the co-products from Animal Feeds and Nutrition Research 53 bioethanol processing. Some detailed results in our studies have been published or are going to be published (Nuez Ortín and Yu, 2009; 2010a, 2010b; Yu and Nuez Ortín, 2010).
CONCLUSIONS AND FUTURE RESEARCH DIRECTION
In conclusion, the new feed evaluation techniques has been developed and continued to be developed to account for the large variation which the traditional/conventional feed analysis methods cannot solved. The new feed development technique such as molecular engineering has been used in developing new feeds to improve feed utilization efficiency. It will continue to this direction. A novel approach to use feed molecular structure as a predictor of the nutrient availability is still in its preliminary stage. In order to obtain more a conclusive predictive equation, a large scale study with various sources of feed is needed to test the applicability of the feed molecular structural parameters investigated. The future research will be focus on quantification of the relationship between feed structures at cellular and molecular levels in relation to nutrient availability. Feed study should be focused on at a molecular level. It is believed that using the advanced technology (SFTIRM) will make a significant step and an important contribution to feed molecular structural-chemical research. The newly developed method could be developed to improve estimating feed value will highly benefit to scientific communication and feed industry.
ACKNOWLEDGMENTS
Our feed research programs have been supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC- Individual Discovery Grant, Canadian federal government), the Ministry of Agriculture Strategic Feed Research Chair Program, the Agricultural Bioproducts Innovation Program (ABIP) of Agriculture and Agric-Food Canada (AAFC), Beef Cattle Research Council (BCRC), the Saskatchewan Agricultural Development Fund (ADF) and Feed Industries.
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DVE/OEB model: A review. Anim. Feed Sci. Technol. 99 (1-4): 141-176 (36 pages) (Scientific Review Article) Yu, P., Christensen, D. A., McKinnon, J. J. and Markert, J. D. 2003a. Effect of variety and maturity stage on chemical composition, carbohydrate and protein subfractions, in vitro rumen degradability and energy values of Timothy and alfalfa. Can. J. Anim. Sci. 83: 279-290. Yu, P., Christensen, D. A. and McKinnon, J. J. 2003b. Comparison of the National Research Council-2001 Model with the Dutch System (DVE/OEB) in the Prediction of Nutrient Supply to Dairy Cows from Forages. J. Dairy Sci. 86: 2178-2192. Yu, P. 2004. Application of advanced synchrotron-based Fourier transform infrared microspectroscopy (SR-FTIR) to animal nutrition and feed science: a novel approach. Bri. J. Nutri. 92: 869-885. Yu, P., Christensen, D. A., Christensen, C. R., Drew, M. D., Rossnagel, B. G. and McKinnon, J. J. 2004a. Use of synchrotron FTIR microspectroscopy to identify chemical differences in barley endosperm tissue in relation to rumen degradation characteristics. Can. J. Anim. Sci. 84, 523-527 Yu, P., McKinnon, J. J., Christensen, C. R. and Christensen, D. A. 2004b. Imaging molecular chemistry of pioneer corn. J. Agric. Food Chem. 52: 7345-7352. Yu, P., McKinnon, J. J., Christensen, C. R. and Christensen, D. A. 2004c. Using synchrotron transmission FTIR microspectroscopy as a rapid, direct and non-destructive analytical technique to reveal molecular microstructural-chemical features within tissue in grain barley. J. Agric. Food Chem. 52: 1484-1494. Yu, P., McKinnon, J. J., Christensen, C. R., Christensen, D. A. 2004d. Using synchrotron- based FTIR microspectroscopy to reveal chemical features of feather protein secondary structure: comparison with other feed protein sources. J. Agric. Food Chem. 52: 7353- 7361. Yu, P., McKinnon, J. J.Maenz, D. D., Racz, V. J. and Christensen, D. A. 2004e. The Specificity and the Ability of Aspergillus Feruloyl Esterase to Release p-Coumaric Acid from Complex Cell Walls of Oat Hulls. J. Chem. Technol. Biotechnol. 79:729-733. Yu, P. 2005a. Applications of cluster analysis (CLA) and principal component analysis (PCA) in feed structure and feed molecular chemistry research using synchrotron-based FTIR microspectroscopy. J. Agric. Food Chem. 53: 7115-7127. Yu, P. 2005b. Multi-component peak modeling of protein secondary structures: comparison of Gaussian with Lorentzian analytical method for plant feed and seed molecular biology and chemistry research. Applied Spectroscopy. 59: 1372-1380. Yu, P. 2005c. Protein secondary structures (α-helix and β-sheet) at a cellular level and protein fractions in relation to rumen degradation behaviors of protein: a novel approach. British J. Nutri. 94:655-665. Yu, P. 2005d. Molecular chemistry imaging to reveal structural features of various plant feed tissues. J. Structural Biol. 150, 81-89. Yu, P., McKinnon, J. J. and Christensen, D. A. 2005a. Hydroxycinnamic acids and ferulic acid esterase in relation to biodegradation of complex plant cell walls. Can. J. Anim. Sci. 85: 255–267. Yu, P, Wang, R. and Bai, Y. 2005b. Reveal protein molecular structural-chemical differences between two types of winterfat (forage) seeds with physiological differences in low Animal Feeds and Nutrition Research 59
temperature tolerance using synchrotron-based FTIR microspectroscopy. J. Agric. Food Chem. 53: 9297-303. Yu, P. 2006. Molecular chemical structure of barley protein revealed by ultra-spatially resolved synchrotron light sourced ftir microspectroscopy: Comparison of barley varieties. Biopolymers. 85(4): 308-317 Yu, P., Block, H., Niu, Z. and Doiron, K. 2007. Rapid Characterization of Molecular Chemistry and Nutrient Make-up and Microlocalization of Internal Seed Tissue. J. Synchrotron Radiation. 14: 382-390. Yu, P. 2008a. Using special degradation ratio system as an alternative method for feed evaluation and diet formulation: A review. Anim. Sci. J. 79: 143-151 (Invited Review). Yu, P. 2008b. Synchrotron-based microspectroscopic analysis of molecular and biopolymer structures using multivariate techniques and advanced multi-components modeling. Can. J. Analytical Sci. Spectroscopy. 53 (5): 220-231. Yu, P., Rossnagel, B. G. and Niu, Z. 2008a. Protein value of a new genotype oat (CDC SO-I) for the NRC dairy model: protein degradation balance and kinetics, protein fractions and total metabolizable protein supply. Can. J. Anim. Sci. 88: 507-513. Yu, P., Doiron, K and Liu, D. 2008b. Shining light on the difference in molecular structural chemical make-up and the cause of distinct biodegradation behavior between malting- and feed-type barley: a novel approach. J. Agric. Food Chem. (Molecular Nutrition Section). 56: 3417-3426. Yu, P. 2010a. Book Chapter: Detect Structural Features of Asymmetric and Symmetric CH2 and CH3 Functional Groups and Their Ratio of Biopolymers within Intact Tissue in Complex Plant System Using Synchrotron FTIRM and DRIFT Molecular Spectroscopy. In: Biopolymers. Accepted. Yu, P. 2010b. Plant-based food and feed protein structure changes induced by gene- transformation, heating and bio-ethanol processing: A novel synchrotron-based molecular structure and nutrition research program. Molecular Nutrition and Food Research (MNF). Accepted. Yu, P. and Nuez-Ortín, W. G. 2010. Relationship of protein molecular structures to metabolizable proteins in different types of dried distillers grains with solubles: A novel approach. British J. Nutri. In Press. Yu, P., Niu, Z., and Damiran, D. 2010a. Protein molecular structures and protein fraction profiles of new co-products of bioethanol production: A novel approach. J. Agric. Food Chem. 58: 3460-3464. Yu, P., Niu, Z. and Christensen, D. A. 2010b. Effects of partially replacing barley or corn with the raw and micronized CDC SO-I oats on productive performance of lactating dairy cows. Archives Anim. Nutri. In press.
In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 61-83 © 2011 Nova Science Publishers, Inc.
Chapter 3
VETERINARY DRUG USE AND ENVIRONMENTAL SAFETY
Sara Leston1,2, Margarida Nunes1,2, Marco F. L. Lemos3,4, Gabriela Jorge da Silva2, Miguel Ângelo Pardal1 and Fernando Ramos2 1CFE - Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Apartado 3046, 3001-401 Coimbra, Portugal 2CEF - Center for Pharmaceutical Studies, Health Sciences Campus, Pharmacy Faculty, Coimbra University, Azinhaga de Santa Comba, 3000-548 Coimbra, Portugal 3School of Tourism and Maritime Technology, Marine Resources Research Group, Polytechnic Institute of Leiria, 2520–641 Peniche, Portugal 4Centre for Environmental and Marine Studies, University of Aveiro, Portugal
ABSTRACT
Presently, the aspects related with human nutrition are of significant importance, focusing the attention of scientists and policymakers regarding the safety of food supplies. Furthermore, the world‘s continuously growing population and the correlated decrease of natural resources have led to the steep increase of animal farming, especially aquaculture. To assure farmed animal‘s health the resort to chemicals and veterinary drugs is frequently taken. This practice in animal production is well described, whether for prophylaxis or therapeutical needs, which dictated the implementation of regulations regarding a responsible use of drugs and chemicals, to reduce the hazards (defined as ―a biological, chemical or physical agent in, or condition of, food with the potential to cause an adverse health effect‖ according to Regulation (EC) No. 178/2002) to consumers. However, the guidelines regarding the hazards of drug use contemplate farmed animals, veterinary drug residues, feeds, withdrawal time and the quality of the water used in aquaculture, neglecting the potential contamination of the surrounding natural ecosystems. Ecologically and economically important species may present similar risks to human health as farmed animals do as they can also accumulate drugs and chemicals, thus representing an increased threat as such species are not subjected to the same safety 62 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al.
controls as are farmed animals. Also, the possibility of a given chemical or drug being accumulated along the food chains (biomagnification) is, in most cases, overlooked. In this chapter, the risks of environmental contamination and food safety will be addressed, in particular for species with significance to human consumption.
NUTRITION AND FOOD SAFETY
Nutrition and food safety are two concepts that are becoming every day more intertwined with the development of the world‘s food supplies since it is internationally recognized that each individual has the right to nutritionally adequate and safe food. In fact, according to the Food and Agriculture Organization of the United Nations (FAO) food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life [World Food Summit, 1996]. This statement focuses on supply, safety and nutrition, three fundamental concepts to the existence of security. Each aspect involves many principles and requirements safeguarded in regulations and legislations at international levels as food is more than ever a global issue. The European Union under the General Food Law Regulation [Regulation (EC) No. 178/2002] defined food as any substance or product, whether processed, partially processed or unprocessed, intended to be, or reasonably expected to be ingested by humans and established the general principles of food law to assure the protection of human health and consumer interest. This regulation also laid down the ground rules for the European Food Safety Authority (EFSA) which has the responsibility to uphold a scientific and technical support system, constituting an independent scientific point of reference regarding the safety of food and feed supply chains as well as environmental protection as a whole [Regulation (EC) No. 178/2002). According to the Codex Alimentarius [FAO/WHO, 2001], food safety is the assurance that food will not cause harm to the consumer when it is prepared and/or eaten according to its intended use. Another important aspect included in General Food Law is feed safety, where feed is defined as any substance or product, including additives, whether processed, partially processed or unprocessed intended to be used as oral feeding to animals. The ultimate purpose of food and feed safety is to uphold human health but it presents itself as a very intricate task as measuring and securing safety is not easy. The basis of the whole evaluation of safety is risk analysis which deals with the detection of hazards, defined as biological, chemical or physical agents in, or condition of, food or feed with potential to cause an adverse health effect and potential risks which are a function of the probability of an adverse health effect and the severity of that effect, consequential to a hazard. In other words, a risk is the negative effect of the occurrence of a hazard. Risk analysis can be broken down in three phases: risk assessment, risk management and risk communication. Risk assessment relates to a scientifically based process consisting of hazard identification, hazard characterization, exposure assessment and risk characterization [Article 3 of the Regulation (EC) No. 178/2002]. This phase is the responsibility of the EFSA, whereas risk management and communication, which are more related to policies and exchange of information, fall into the European Commission, Council and European Parliament duties. Scientific studies are a decisive tool in assessing risk as well as in identifying possible emerging risks through the collection, analysis and treatment of relevant data, particularly regarding: food consumption Veterinary Drug Use and Environmental Safety 63 and exposure, biological risks, contaminants in food and feed as well as residues {Articles 33 and 34 of the Regulation (EC) No. 178/2002]. The concept from the farm to the fork summarizes the new views on food safety. More than ever, safety must be addressed along the entire food chain, from primary production to final consumption. With this broaden view, instead of concentrating the responsibility for safe food in the processing sector, all the sectors involved are now held accountable for safety [FAO, 2003a]. Instead of focusing on the end-product, control points should be placed along the food production process from primary production to the consumer‘s table. In fact, this is the principle of the hazard analysis critical control points (HACCP), an effective approach already recognized in the food industry for many years and recommended by FAO as preventive measures in safety control [FAO, 2003a; Burlingame and Pineiro, 2007]. Also, with this holistic food chain perspective, changes will be expected in all of the steps involved to guarantee more nutritious and safe food, including more viable environmental and economical practices [FAO, 2003a].
VETERINARY MEDICINAL DRUGS
One issue closely related with food safety is the use of chemicals in food animal production which can constitute potential hazards for human health. The resource to drugs is undeniably necessary to assure the welfare of animals, whether administered to prevent (prophylaxis) or to treat (therapeutics) disease, but the awareness that veterinary medicinal drugs (VMD) can be potentially prejudicial, dictating the need to regulate their production, distribution and administration to safeguard public health, uphold in the EU under the Directive 2001/82/EC [Regulation (EC) No. 82/2001]. This regulation defines VMD as any substance or combination of substances presented as having properties for treating or preventing disease in animals and set the rules for their safe use and marketing [Regulation (EC) No. 82/2001]. Another important regulation concerning VMD is the Council Regulation (EC) 2377/90 which aimed to prevent hazards associated with the consumption of unsafe residues in edible tissues by establishing their maximum residue limits (MRL) in food of animal origin. This limit refers to the maximum concentration of residue resulting from the use of a veterinary product (expressed in mg/kg or µg/kg on a fresh weight basis) legally permitted without any harm to consumer, including food of environmental origin [Regulation (EC) No. 2377/90]. The marker residue can be either the original drug or a resulting metabolite that best characterizes the depletion from the edible tissues. This regulation divided VMD according to their pharmacologically active substances which were grouped into four annexes according to their MRL. Annex I included all of the substances to which full MRL have been fixed, whereas Annex II incorporated those drugs whose evaluation indicated that no limit is required in order to protect public health. Substances in Annex III had a provisional MRL with a defined period of time not exceeding five years until scientific studies were completed (if necessary to the conclusion of the studies an additional two year period could be granted). Finally, substances included in Annex IV were considered a hazard to consumer‘s safety as the presence of residues in foodstuff of any origin can be very harmful and no limit could be established. The administration of substances in Annex IV was therefore prohibited in the EU [Regulation (EC) No. 2377/90]. Another important regulation 64 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al. related with drugs safety is Regulation (EC) No. 726/2004 which dictated the procedures for authorization and supervision of medicinal products for human and veterinary use. Moreover, this regulation established the European Medicines Agency improving the previously existing European Agency for the Evaluation of Medicinal Products [Regulation (EC) No. 726/2004]. Recently, as a result of years of research and technical advances in several areas, new regulations were set to improve and in some cases, to revoke previously existing regulations. Regulation (EC) No. 470/2009, based on the 2377/90, laid down the procedures to establish the residue limits for pharmacologically active substances also taking into account risk assessment and management [Regulation (EC) No. 470/2009]. Early this year, Commission Regulation (EC) 37/2010 was adopted regarding pharmacologically active substances in foodstuffs of animal origin. Under this decision, substances are classified following the system set by Regulation (EC) No. 470/2009, according to their MRL and, in the interest of clarity and simplification, are listed alphabetically in only one Annex divided in two tables for allowed and prohibited use incorporating the four annexes set by the now repealed Regulation (EC) No. 2377/90 [Commission Regulation (EC) 37/2010]. Nonetheless, even with all the regulations and legislations set to safeguard human and veterinary health and to establish consumer‘s trust, over the last decades several incidents have occurred with chemicals found in food. For instance, clenbuterol (4-amino-(t- butylamino)methyl)-3,5- dichlorobenzyl alcohol hydrochloride), a β2 adrenergic agonist permitted in the EU as a bronchodilating and tocolytic agent in both human and veterinary medicine has been administered to other ends other than the allowed [Ramos et al., 2003; Barbosa et al., 2005]. Since its actions include muscular relaxation, it was used as a growth promoter to increase muscular mass and diminish the accumulation of fat making meat more lean and tender. As a result of this illegal application, several cases of intoxication were reported in Portugal [Ramos et al., 2003; Barbosa et al., 2005], Spain [Martínez-Navarro, 1990; Salleras et al., 1995], Italy [Brambilla et al., 2000] and France [Pulce et al., 1991] after ingestion of liver and meat with high residue contents. Other well-known incidents occurred with the detection of nitrofuran residues in meat and other food products [Cooper et al., 2004; Barbosa et al., 2007]. Nitrofurans are a group of nitroheterocyclic drugs with a broad spectrum activity against bacterial and protozoan infections, in humans and animals [Ræther and Hänel, 2003; Vass, 2008]. However, since studies pointed out to the possibility of mutagenic, teratogenic and carcinogenic effects linked to the use of nitrofurans, they were included in Annex IV of Regulation (EC) No. 2377/90 [Commission Regulation 1442/95] and therefore banned from use in livestock production in the EU. As part of the risk assessment of nitrofurans, the international project FoodBRAND was launched with the participation of several member states [O‘Keeffe et al., 2004; Barbosa et al., 2007]. During the project‘s timeframe (2000-2003), new methods for the determination of residues for both the parent drug and resulting metabolites were developed and implemented for routine inspections [Cooper et al., 2004].
AN OVERVIEW ON AQUACULTURE
The combination of population growth, increasing urbanization and rising incomes has contributed to a greater consumption of animal products. World population doubled during Veterinary Drug Use and Environmental Safety 65 the last 45 years and the latest United Nations projections anticipated that the world population will rise from 6.8 billion today to 9.1 billion in 2050. At the same time, urbanization is a major force in global food demand. The growing urbanization tends to change lifestyles and modify food habits: compared with the less-diversified diets of rural population, city dwellers usually have a more diverse diet, richer in higher-energy foods, with more proteins from meat, fish and milk, and fewer carbohydrates and fibers . Furthermore, economic development and increasing wealth usually enhance the availability and quality of food, better overall nutritional status and the mitigation of food deficiencies. This is normally accompanied by improvements in the marketing, production, processing and food distribution. For instance, the number of supermarkets has escalated in numerous developing countries, mainly in Asia and Latin America, offering a wider choice, lower prices and safer food products, targeting a broader range of consumers [FAO, 2009]. In accordance with the above-mentioned, global trends in the consumption of animal products, worldwide fish consumption has increased from 9 kg per capita per year in the early 1960s to 16.7 kg in 2006, and it is expected to rise to 17.0 kg by 2020 [Delgado et al., 2003; FAO, 2009]. However, the demand for fish increases at a higher rate than wild fish stocks can support. As stated by FAO, the maximum wild capture fisheries potential from the world‘s oceans has probably been reached. Overall, 80% of the world fish stocks for which assessment information is available are reported as fully exploited or overexploited [FAO, 2009]. FAO is the only intergovernmental organization undertaking regular worldwide collection, compilation, analysis and diffusion of data and information of fisheries and aquaculture. With the depletion of wild stocks due to overfishing and failed management practices in the capture fisheries, aquaculture offers the only viable alternative to the ever- increasing demand for fishery products. As a result, during the past decades, aquaculture has expanded, diversified, intensified and made technological advances. Similar to other animal production systems, such as pork, poultry, and eggs, aquaculture has grown into a highly globalized industry. In fact, aquaculture is growing more rapidly than any other animal food- producing sector in the world. According to FAO statistics, this sector has grown at an average rate of 8.9% per year since 1970, compared with only 1.4% for capture fisheries and 2.8% for terrestrial farmed meat production systems over the same period [FAO, 2003b]. In 2006, total fisheries production (capture fisheries and aquaculture production) supplied about 110 million tons of food fish, providing the highest per capita supply recorded. After growing steadily in the last four decades, aquaculture contributes for the first time to approximately half of the fish available for human consumption worldwide, while in the 1970s, it accounted for only 6% [FAO, 2009]. The global aquaculture production is dominated primarily by countries located in the Asia-Pacific region, which accounted for 89% of total production in 2006. This dominance is mainly due to China‘s enormous production [Broughton and Walker, 2010]. Farmed fish have the lowest feed conversion efficiency (kg of grain per kg of body weight) among intensively fed livestock animals. As a consequence, in the last decades aquaculture has contributed significantly to global food security, poverty reduction, and has provided employment opportunities and income generation in different parts of the world, particularly in developing countries [FAO, 2003b]. Furthermore, aquaculture has a recognized importance as a source of healthy food for human consumption. Fish and fishery products provide a valuable supplement for nutritious diets and their diversification. With a nutritional profile superior to any terrestrial meat product, fish represents not only a vital 66 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al. source of high-quality proteins, but also contributes with a wide range of essential micronutrients (vitamins A, D, E and B complex), minerals (calcium, phosphorus, iron, iodine and selenium) and fatty acids [Tacon et al., 2001]. The dietary contribution of fish is more relevant in terms of animal proteins, which is a particularly important component in some densely populated countries where total protein intake levels is low. In fact, even consumed in small quantities, fish often comprises a nutritionally important part of the daily diet in developing countries, representing an affordable source of animal protein and providing essential amino acids that are often present only in low quantities in vegetable- based diets. According to FAO [2009], despite of the relatively low-level of fish consumption, the contribution of fish to total animal protein intake in low-income food-deficit countries accounts for approximately 20 percent. Moreover, in some coastal and island developing states (including Bangladesh, Indonesia, Senegal, and Sri Lanka), it provides over 50% of total animal protein intake, reaching over 60% in Gambia, Sierra Leone and Ghana. Fish consumption is also recognized as highly beneficial since it constitutes the major source of omega-3 polyunsaturated fatty acids (n-3 PUFAs) such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the human diet [Kris-Etherton et al., 2002]. These two kinds of n-3 PUFAs are beneficial for brain development in the foetus and newborn and has been demonstrated to have protective effects in preventing coronary heart disease, reducing arrhythmias and thrombosis, lowering plasma triglyceride levels, preventing mental disorders and various inflammatory conditions such as bowel disease, asthma, and arthritis, and even decreasing risks of certain cancers [Berquin et al., 2008; Kris-Etherton et al., 2002; Sinclair et al., 2007; Ruxton et al., 2004; Uauy et al., 2001]. Therefore, several health organizations such as the American Heart Association (AHA), recommend eating fish at least twice a week for the general population [Kris-Etherton et al., 2002; Harris, 2004]. According to FAO, the term aquaculture encompasses all activities associated with the farming of aquatic organisms such as fish, mollusks, crustaceans, aquatic plants, crocodiles, alligators, turtles, and amphibians. Farming implies some form of intervention in the rearing process to increase the productivity of these organisms beyond the natural capacity of the environment, such as control of breeding, regular stocking, protection from predators, and supply of artificial feed and medication [FAO, 1997]. Fish farming has been practiced since ancient times. For instance, there is evidence that common carp was raised for food in China during the period 2000–1000 B.C.. The traditional aquaculture systems were small, farm-size with low stock density and minimal added inputs. However, the early forms of aquaculture differed greatly from those practiced today. While, until recently, there were no reasons for the development of intensive fish farming techniques, the increased demand for aquaculture products has contributed to an unprecedented growth in this industry over recent decades. Thus, current intensive aquaculture is not only characterized by high stock density and volume, but also by the heavy use of formulated feeds containing antibiotics, antifungals and other pharmaceuticals, and the application of pesticides and disinfectants. These chemicals used in aquacultures enable more control over the fish development, i.e. to enhance and control production in hatcheries, increase feeding efficiency, improve survival rates, decrease diseases and reduce transport stress [Huntington et al., 2006]. Unfortunately, the intensification of production methods has been accompanied by an increase in the potential food safety and human health concerns associated with the consumption of aquacultured products. The majority of intensive aquaculture facilities rely heavily on the input and application of several chemicals. As a result, aquaculture food Veterinary Drug Use and Environmental Safety 67 products can have elevated levels of veterinary drugs and agrochemicals residues, antibiotic- resistant bacteria, persistent organic pollutants (POPs), and heavy metals compared to their wild counterparts [Easton et al., 2002; Hites et al., 2004]. The potential health effects to the consumers can range from chronic health outcomes associated with chemical exposures to the development of infections caused by antimicrobial resistant bacteria [Foran et al., 2005; Samanidou and Evaggelopoulou, 2007]. The dispersion of these chemicals in the environment is another potential key impact of current aquaculture practices. In addition, beyond human health issues, current aquaculture practices may pose environmental and ecological issues including reductions in water quality [Tovar et al., 2000], pressure on feed resources [Naylor et al., 2000], genetic interaction between wild and escaped aquaculture conspecifics [Youngson et al., 2001], disease transfer to wild fish [Krkosek et al., 2007], eutrophication [Loya et al., 2004] and destruction of natural habitats [Paez-Osuna, 2001]. In spite of the potential health benefits of frequent fish consumption, it is worth asking whether the chemicals used in aquaculture practises, as well as other contaminants, can have a negative impact on fish and consumer‘s health. Numerous studies have documented the occurrence of prescribed compounds and unintended contaminants of feed in aquaculture species for human consumption [Esposito et al., 2007; Bernstssen et al., 1999; Jacobs et al., 2002; Carubelli et al., 2007]. Veterinary drugs have been used in aquaculture mainly for therapeutic purposes and as prophylactic agents. However, their extensive administration represents a potential threat to human health. The use of antimicrobials in aquaculture is associated with the emergence of antibiotic-resistant bacteria in aquaculture environments, an increase in antibiotic resistance in fish pathogens, as well as the transfer of these resistance determinants to human pathogens [Cabello, 2004, 2006]. Moreover, antibiotic residues can persist in edible animal tissues [Samanidou and Evaggelopoulou, 2007]. The consumption of farmed fish may also result in exposure to a variety of POPs that might represent health risks for the consumers [Hites et al., 2004; Carubelli et al., 2007]. Some studies have shown that feed is the main source of these contaminants in farmed fish [Hites et al., 2004; Antunes and Gil, 2004]. Consequently, some strategies are being developed on how to produce feed low in undesirable compounds, such as, the replacement of fish oil with vegetable oil. Plant oils have substantially lower POPs levels, and thus have great potential to reduce the contamination in feed [Berntssen et al., 2005]. Ecologically and economically important species may represent the same risks to human health as aquaculture fish do as they can also accumulate chemicals released by aquaculture facilities into the aquatic environment [Katranitsas et al., 2003; Fortt et al., 2007]. Many antibiotics used in aquaculture are stable chemical compounds and have been shown to persist in water and sediment for several weeks following administration [Björklund et al., 1990]. As a result, they may enter the aquatic food web leading to exposure of non-target biota around aquaculture sites. In fact, wild fish harvested from areas around aquaculture facilities and destined for human consumption, have been reported to contain concentrations of antibiotics beyond safe levels [Fortt et al., 2007]. The residues of antibacterial agents may also affect the sedimentary microbial community and introduce antibiotic resistance in bacteria [Tendencia and de la Pena, 2002]. Furthermore, the occurrence of resistant bacterial populations in sediments around fish farms can lead to the transmission of resistant bacterial strains to human populations through the consumption of fishery products.
68 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al.
PHARMACEUTICALS FATE IN THE ENVIRONMENT
Emerging contaminants include all the chemical compounds which can be found in the environment with the potential to cause adverse effects but that were not, until recently, monitored in environmental risk assessment studies. However, in the last decades the interest in these substances has grown considerably and with it the need to understand the effects they can produce on biota [Boxall et al., 2004]. VMDs are part of this emerging class of environmental contaminants which have existed in the ecosystems for as long as they have been administered. The properties which make drugs effective are also the same that can become a concern when these substances are released into the ecosystems. For instance, drugs are designed to alter specific biochemical pathways in the target species for which their use is intended but when released into the environment, non-target organisms are likely to be affected. Moreover, drugs are biologically active and present in low partition coefficients which when discharged in the water confers them higher mobility. For all these reasons, environmental risk assessment of VMDs is now mandatory in the EU [Regulation (EEC) No 81/852 as amended by Regulation (EEC) No 92/18] and in other countries, including the USA and Canada. In fact, between 1999 and 2000, the United States Geological Survey conducted a study indicating that out of a network of 139 streams across 30 states, 95 were contaminated with traceable concentrations of antibiotics [Kolpin et al., 2002]. The increasing amount of evidences in the environment made it clear that much research is needed to assess the impact of antibiotics released into the environment, their potential effects on wildlife and ultimately on human health. Although, the use of antibiotics in fish farminghas fallen over the last years, it has been estimated that livestock producers in the United States use 24.6 million pounds of antimicrobials every year for non-therapeutic purposes [Mellon et al., 2001]. A wide variety of pharmaceuticals used in human and veterinary medicine has been found in diverse environmental compartments such as water, soil and sediments. Chemicals reach these compartments through several distinct activities (Figure 1), with significant amounts (up to 75%; Elmund et al., 1971; Feinman and Matheson, 1978] being excreted as active metabolites. Antibiotics are also present in sewages, largely as a result of human excretion. Many active antibiotics are not completely metabolized during therapeutic use and thus enter sewage through excretion in an unchanged form. Veterinary use and the intentional disposal of unused drugs into the sewer also contribute to the quantities of antibiotics found. Discharges from veterinary clinics and runoff from agricultural applications into municipal sewers are also potential sources of veterinary antibiotics in wastewaters [Le-Minh et al., 2010]. These compounds can also enter the aquatic environment when medicated feed sinks to the water body bed or is eliminated by fish excretion in aquacultures [Smith and Samuelesen, 1996]. Drugs used in fish farms or their sub-products can also be transported directly into surface water or accumulate in the sediment [Björklund et al., 1990; Jacobsen and Berglind, 1988]. In these studies, a range of 0.3–16 mg/kg of oxytetracycline was found in the sediments located downstream of a fish farm. Additionally, several studies report that substances extensively applied in fish farming present long half-lives in soil and sediment [Marengo et al., 1997; Samuelsen et al., 1992, 1994; Hektoen et al., 1995; Jacobsen and Berglind 1988; Capone et al., 1996].
Veterinary Drug Use and Environmental Safety 69
Antibiotics Man
Intensive Aquaculture Pasture Sewage farming
Soils Soil Fertility Improvers
Water Intake through crops/food and drinking water
Sediments
Figure 1. Route and fate of antibiotics in the environment.
Parameters such as route of drug administration, drug formulation and pharmacokinetics, the dose applied and the frequency of treatment also have influence on the concentration of these chemicals in soils, sediments, surface and ground waters [Kim and Carlson, 2007]. Another entry route of these drugs into the environment is land-based forms of livestock production, where, in intensive farming, the active ingredients or metabolites might reach the soil by leaching from animal slurry and manure. The excretion ratio will vary depending on the compound but it can be as high as 80-90% of the parent compound, being excreted via urine and feces [Heberer, 2002; Bound and Voulvoulis, 2004]. Measured concentration of veterinary products in animal waste or manure from previous studies has ranged from 11 to 12400 mg/kg [Haller et al., 2002; Schlusener et al., 2003]. Residues of two antibiotics extensively used in livestock production were recently detected in soil fertilized with animal slurry [Hamscher et al., 2002]. This study found an average concentration of 198.7 mg/kg of tetracycline and 4.6–7.3 mg/kg of chlortetracycline at a soil depth of 10–20 cm. Authors concluded that when liquid manure is applied repeatedly, antibiotics could enter the environment in significant concentrations and accumulate persistent residues in the soil. Another study also measured the concentrations of chlortetracycline and tylosin in soil taken from manure-amended fields and found 0.6–15.5 mg/kg for chlortetracycline and 1.8–57.4 mg/kg for tylosin during the following 146 days period (Jacobsen et al., 2004). Farm animals kept in pastures may also contribute as a soil contamination point-source directly through their excrements. McKellar [1997] reported no significant degradation over a 45-day time period for residues of the anti-parasitic ivermectin in feces of cattle treated with pour-on or subcutaneous preparations while Kolar and Erzen [2006] established a DT50 value of 23 and 22 days for dissipation of the also anti-parasitic abamectin and doramectin from sheep feces 70 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al. under field conditions and in the pasture, respectively [Bound and Voulvoulis, 2004]. The use of sewage sludge as a soil fertility improver has been a practice in the EU, as long as specified requirements are fulfilled [Council Regulation (EC) 86/278]. Nevertheless, these do not include analysis to the concentration of antibiotics present, and it has been proved that the use of sewage sludge as soil fertilizer contributes as an antibiotic contamination point-source mechanism. Research has shown that after passing through wastewater treatment, pharmaceuticals, amongst other compounds, are released directly into the environment [Kümmerer, 2009]. Soil may become an entry route for these drugs, when they leach into the aquatic environment of nearby water courses or groundwater‘s and, in turn, to the sediments. As a result, antibiotics consumed by humans and animals can be introduced into different environmental compartments depending on their physicochemical properties. Their environmental fate (behavior, persistence in the environment, and toxicity) will depend greatly on their physical/chemical characteristics as well as of the medium they are in [Kim and Carlson, 2007; Kümmerer, 2009]. The octanol/water partition coefficient (Kow) has become a key parameter in organic chemicals environmental fate assessment studies and is defined as the ratio of a chemical's concentration in the n-octanol phase (a surrogate for lipids) to its concentration in the aqueous phase of a two-phase octanol/water system [Tolls, 2001]. It can be potentially related to water solubility, partition into lipids and fats, soil/sediment adsorption coefficients, and bioconcentration factors for living beings. Kow values per se can be considered meaningful, since they represent the tendency of the chemical to partition itself between the various environmental compartments. Therefore, chemicals with low Kow values (i.e., lower than 10) may be considered relatively hydrophilic, having high water solubilities, small soil/sediment adsorption coefficients, and small bioconcentration factors. Conversely, chemicals with high
Kow values (e.g., greater than 104) are very hydrophobic (for a review in measured sorption coefficients values see Boxall et al., 2004). This parameter, by including water solubility, is of great importance to understanding organic chemicals mobility in soils and sediments. Moreover lipophilicity may represent great value to predict bioaccumulation in living tissues. From the Kow, it is possible to derive the distribution coefficient (Kd) which stands for the ratio between the contaminant concentration in the soil or sediment and the chemicals concentration in water, representing the partitioning between these two environmental compartments. The fate of the contaminants and their degradation products will depend on these partitioning characteristics and these measures have been considered to be an important tool to understanding the fate of a given pharmaceutical in the environment and estimate the probable concentration in different compartments. For instance, sulphonamide antibiotics have a Log Kow of 0.12-1.5 [Le-Minh et al., 2010], and therefore are likely to be mobile and leach from dung and soil to groundwater, posing less risk of soil contamination but on the other hand, with more chance to be found in surface and ground waters, carrying greater risk to the aquatic compartment. Tetracyclines, macrolides, and fluoroquinolone antibiotics will be rather immobile in the soil [Kümmerer et al., 2000], and consequently, they are not readily leached into ground water and are accumulated in the soil, what will pose distinct environmental fate and thus, problems. High Kow values cause limited aqueous solubility of these compounds. Therefore, when drug residues reach the environment they tend to be adsorbed on soil or sediment particles.
Nevertheless, there are cases where the Kow might not be sufficient to predict for complex structures behavior, such as pharmaceutical compounds, as they generally possess multiple Veterinary Drug Use and Environmental Safety 71 ionization sites and these ionic interactions are also possible sorption mechanisms. Some antibiotics are large and complex chemical molecules that may even contain acidic and basic groups in the same molecule. Therefore, distribution between the solid and the aqueous phase will also depend greatly on pH, redox potential, the stereo chemical structure, and the chemical nature of both the sorbent and the sorbed molecule. Sorption properties have been examined in natural soil types with a variety of proportions of sand and clay, and results show a dependence of the aerobic degradation with the type of soil [Taylor, 1999]. The sorption of antibiotics is also especially affected by the amount and nature of free and suspended particles in the water phase and soil organic matter (SOM) and soil minerals and Kd [Thiele-Bruhn, 2003] and may have an impact on the spread and (bio)availability of pharmaceuticals in the environment. Some antibiotics, e.g., tetracyclines, are known to have a tendency to bind to soil particles or to form complexes with the ions present [Thiele-Bruhn, 2003; Rabølle and Spliid, 2000; Tolls, 2001; Ter Laak, 2006a, 2006b]. Therefore, binding to particles or the formation of complexes may cause a loss in detectability, as well as a loss in antibacterial activity but does not necessarily indicate biological or photochemical degradation. The loss of antibacterial activity, for example, was demonstrated for an aquaculture antimicrobial assay in seawater driven by the formation of complexes with magnesium and calcium present in marine water [Lunestad and Goksøyr, 1990]. The biodegradation of antibiotics in the sediments is a relatively slow process (a half-life of up to 150 days in the topmost sediment layer, 0-1 cm), but varies substantially among the various agents and among types of sediment. A study in which samples were taken up from under various fish farms indicates that florfenicol has a half-life a fraction of that of other common agents [Hektoen et al., 1995]. Moreover, under anaerobic conditions many antibiotics occurring in soil and sediment proved to be quite persistent [Gartiser et al., 2007]. Nevertheless, some antibiotics, such as -lactams, do not follow this pattern of persistence since their degradation takes place under acidic and alkaline conditions or by reactions with weak nucleophiles, such as water, as well as by enzymatic degradation [Le-Minh et al., 2010]. The environmental fate of pharmaceuticals, besides being affected by the intrinsic characteristics of the sorbent and sorbed molecule will also be influenced by other external factors, such as climate conditions (e.g. light intensity, temperature, etc). Ivermectin, for instance, will undergo rapid photodegradation as a thin, dry film on a glass, presenting a half-
life (DT50) of 3 h and in the surface water its photodegradation DT50 is 12 h in the summer and 39 h in the winter [Halley et al., 1989]. The same authors also report a half-life of ivermectin in soil or feces-soil mixture in the range of 91 to 217 days in the winter and 7 to 14 days in the summer. Another study reports a half-life for abamectin degradation in sandy loam, clay and sand soil ranging from 20 to 47 days, and no significant degradation in sterile soil, indicating that soil organisms are responsible for its degradation [Fisher and Mrozik, 1992].
ANTIMICROBIAL RESISTANCE IN BACTERIA OF ANIMAL ORIGIN: POTENTIAL RISKS FOR ENVIRONMENT AND PUBLIC HEALTH
Antibiotics are essential for the treatment of bacterial infections in humans and animals. Beyond the use of drugs for therapeutic purposes in the human and animal clinical settings, 72 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al. antibiotics are also used extensively as prophylactic agents and animal growth promoters in agriculture and food producing industries. The intensive use and misuse of antibiotics is recognized as a major driving force for the development and selection of bacterial resistance to antimicrobial agents. The last decades have witnessed an ever-growing emergence of antimicrobial resistance worldwide. Increasing evidence suggests that this may contribute to the dissemination and augmentation of antimicrobial resistance in the environment. Furthermore, the development of antimicrobial resistance causes increased treatment failures and reduces therapeutic options, which raised human and animal morbidity and mortality, and inflated the costs associated with the maintenance of public health. A better understanding of the mechanisms leading to bacterial resistance, and the underlying molecular processes responsible for the development and dissemination of antimicrobial resistance are sorely needed to enable a more realistic assessment of the environmental risks and public health issues created by the use of antibiotics in intensive animal farming. Mechanisms of resistance to antibiotics include: the production of inactivating enzymes, the modification of the drug target, the reduced bacterial cell permeability and the increase efflux of the drug from the cell [Guardabassi and Courvalin, 2006]. Some of these mechanisms are specific for a class of antibiotics or members of a single class. For example, ß-lactamases hydrolyze ß-lactam antibiotics, with each enzyme type showing its own spectrum of activity [Gray et al., 2004]. Other mechanisms of resistance can be non-specific, conferring resistance to antibiotics with various molecular structures. For example, macrolides, lincosamides and streptrogramins B (MLSB) are structurally different but functionally similar drugs, all binding to the bacterial 50S ribosomal subunit. Consequently, the methylation of a single residue in 50S rRNA will confer resistance to all three groups [Leclercq and Courvalin, 1991]. This phenomenon whereby resistance is conferred by one single biochemical mechanism against drugs of different structures is known as cross-resistance. Bacteria can acquire antimicrobial resistance by DNA mutation or by horizontal gene transfer. Mutations occur spontaneously, at variable frequency, depending on the antibiotic and the microorganism. For instance, resistance to nalidixic acid or streptomycin is a relatively common event in contrast with vancomycin. Sometimes, bacteria will need to accumulate mutations in a stepwise process to develop fully defined clinical resistance, e.g. in the resistance to fluoroquinolones [Jacoby, 2005]. Horizontal gene transfer involves the acquisition by the bacterial cell of foreign DNA, a phenomenon that may occur via three mechanisms: transformation (capture of free DNA), transduction (via bacteriophage DNA) and conjugation. The latter event plays a crucial role in the evolution of environmental bacteria, and the spread of resistance determinants [Sobecky and Hazen, 2009; Barlow, 2009]. Resistance traits located in genetic mobile elements like plasmids, transposons or integrons can be transferred to different strains or bacterial species [Frost et al., 2005]. Food of animal origin may act as reservoirs for resistant zoonotic bacteria and has been considered one of the main vehicles for human transfer [de Jong et al., 2009; Ewers et al., 2009; Châtre et al., 2010; Kojima et al., 2010]. More research is needed to indisputably demonstrate whether and how sub-inhibitory concentrations of antibiotics present in the gut of farmed animals for prolonged periods may select for resistant bacteria. Regardless, resistant bacteria can be transmitted to humans from animals by direct contact, through the food chain, or indirectly via water and/or soil contaminated with animal waste. Meat and dairy products can be contaminated by bacteria from animal origin during slaughtering or Veterinary Drug Use and Environmental Safety 73 milking processes [Wu et al., 2009]. Uncooked or undercooked food may be the source of zoonotic human pathogens like Salmonella sp., Campylobacter sp., Listeria sp. and Escherichia coli [Tauxe, 2002]. Food-borne illnesses are usually self-limiting, and the administration of an antibiotic may not be necessary, unless the infection persists or spreads beyond the intestinal barrier. Intestinal commensal bacteria, like Lactobacillus sp. or Enterococcus sp., may also contaminate food-products of animal origin, and possibly play a role in the dissemination of resistance to human pathogens. The use of avoparcin, a glycopeptide used in Europe twenty years ago as a feed additive, triggered the appearance of vancomycine-resistant enterococci (VRE). Vancomycine is also a glycopeptide, and it is mostly used in human medicine as a last resource weapon for the treatment of methicillin- resistant Staphylococcus aureus (MRSA) infections. Beyond the concerns raised by the increased incidence of VRE infections over the recent years, there is the potential added risk of plasmid transfer from VRE to MRSA. As a consequence of the development of such cross- resistance, avoparcin was withdrawn in the late 90s as a feed additive in countries of EU [Phillips, 2007]. However, VRE tend to persist in animal environments due to the genetic linkage that exists between glycopeptides resistance genes and other genes that encode for resistance to other, structurally different antibiotics, like the MLSB. The use of the macrolide tylosin selects for the conjugative plasmid carrying the vanA and ermB determinants [Aarestrup, 2000]. Thus, it is plausible that the use of a single antibiotic may also co-select diverse genetic traits in bacteria, such as virulence factors, or resistance to biocide or heavy metals, if their encoding genes co-exist in the same genetic platform [Bonnet et al., 2009; Baker-Austin et al., 2006]. The antibiotic selective pressure may also occur in the environment, where these effects are more difficult to assess. Some antibiotics used in animal farming management practices, like fluoroquinolones, reach the environment at low-level concentrations in their active form through animal wastes, and land application of non-treated manure, thus allowing bacteria of different origins to mix and exchange genes [Martínez, 2008; Chee-Sanford et al., 2009]. Therefore, the heavy use of prophylactic antibiotics in aquaculture, where drugs are delivered directly into the water or in medicated feeds, may become particularly relevant [Cabello, 2006]. Aquaculture is an important growing industry in many developed and developing countries, especially in Asia and South America, where the use of antimicrobial agents are often unregulated [Heuer et al., 2009]. Also, it is not uncommon in some developing countries to find food-animal facilities, such as piggeries, located near the water ponds. This may further contribute to the enrichment of the water with resistant bacteria from the waste of these terrestrial animals [Phuong et al., 2008]. Compared to the research on terrestrial farming, the potential human health and environmental hazards due to the use of antimicrobial agents in aquaculture have received little attention. However, aquatic bacteria are not different in their response to exposure to antibiotics, and are capable of acquiring and transferring antimicrobial-resistance genes. Research findings also suggest that antibiotics that accumulate in the sediment of the pond from feed spill-over and fecal material, may exert further selective pressure in the mud and shellfish bacteria [Le et al., 2005; Sørum, 2006]. Such a phenomenon may provide new opportunities to the exchange of resistance genes between fish pathogens and the environment. It is well documented that fish pathogens and other aquatic bacteria, like Aeromonas salmonicida, Aeromonas hydrophila, Vibrio anguillarum, Vibrio salmonicida, 74 Sara Leston, Margarida Nunes, Marco F. L. Lemos et al.
Flavobacterium psychrophylum, Pseudomonas fluorescens and Citrobacter freundii may acquire resistance to sulphonamides, quinolones, tetracyclines and trimethoprim [Sørum, 2006]. Aeromonas sp. strains from aquaculture systems are often multidrug-resistant by carrying multiple resistance genes in plasmids and integrons [Sørum, 2006; Gordon et al., 2007; Jacobs and Chenia, 2007]. Therefore, resistance in fish pathogens obviously compromises the effectiveness of antibiotics used as prophylactic agents. Aquatic bacteria may be directly pathogenic to humans (e.g., Vibrio cholerae, Vibrio parahaemolyticus, Salmonella enterica serotype Typhimurium DT104), or act as opportunistic pathogens, such as Aeromonas hydrophila. It has been demonstrated that conjugative plasmids from diverse fish pathogens carrying multiple antimicrobial resistance genes could be transferred in vitro to E. coli, an inhabitant of the human gut, and to other human pathogenic bacteria, such as V. cholerae and V. parahaemolyticus [Heuer et al., 2009]. Resistance in fishery products from aquaculture is of particular significance to human health, since raw or undercooked shellfish represent a common cause of food-borne disease outbreaks. Ready-to-eat shrimps and shellfish cultured in some farming facilities from Thailand and Vietnam contained high numbers of human pathogenic bacteria resistant to multiple antibiotics used in human medicine [Van et al., 2007; Soonthornchaikul and Garelick, 2009]. Effluents of aquaculture systems containing considerable amounts of antibiotics and resistant bacteria may contaminate natural water streams, potentially altering microbial ecosystems [Gordon et al., 2007]. Antimicrobial resistance has its roots in environmental microorganisms, a concept recently defined as the resistome [Wright, 2007]. In fact, recent findings indicate that some antibiotics in very small concentrations may act as cell-signaling molecules in environmental microbial communities [Yim et al., 2007]. Given the enormous number and variety of environmental bacteria, and the multiple mechanisms of gene exchange, the direct consequences on the natural micro-ecosystems of the release of antibiotics and resistant bacteria of animal origin, as a consequence of intensive farming activities, are not easy to predict. The role of the global trade of animals in the dissemination of drug resistance must not be overlooked. It represents a significant route of the worldwide dissemination of antimicrobial resistance [Duran and Marshall, 2005; Boinapally and Jiang, 2007]. Salmonella enterica serotype Typhimurium DT104 is an example of a successful multi-drug resistant strain that might have emerged due to the heavy use of florfenicol in aquaculture [Threlfall, 2000; Cabello, 2009]. Most isolates of this strain possess a chromosomal gene cluster inserted in a mobile element that encodes for resistance to five antimicrobial compounds of different families: ampicillin, cloramphenicol or florfenicol, streptomycin, sulphonamides and tetracyclines (ACSSuT phenotype) [Briggs and Fratamico, 1999]. In view of the environmental risks and public health implications of the use of antibiotics in animal-food production, alternative methods for reducing the emergence and global spread of severe infections are required. Such alternatives may include the use of bacteriophages, photodynamic therapy and vaccines [Almeida et al., 2009; Kurath, 2008]. The development of improved tools to assess the risk of dissemination of resistant bacteria in human, animal and environmental settings, as well as effective international cooperation to assist developing countries in the design and implementation of preventive measures, may offer additional means to manage this upcoming public health concern.
Veterinary Drug Use and Environmental Safety 75
CONCLUSION
The raising of an environmental apprehension over the last few decades has spread to the global antibiotic contamination scenario, concerning not only the environment but also human health, since their entry, directly or indirectly into the human food chain is no longer just a remote possibility. Pharmaceuticals have been shown to have effects on reproduction, biological function and survival of non-target aquatic and terrestrial organisms which have an important role in the food web (Lopes et al., 2009; Han et al., 2010; Römbke et al., 2010). Interruption of the food web may affect the diversity of a system which ultimately may disrupt ecological services and human health (Muñoz et al., 2009; Rodriguez-Mozaz and Weinberg, 2010; Zhao et al., 2010). Another serious concern is the public health risk associated with the increasing number of resistant bacteria found in all the compartments of ecosystems. The transmission of resistant strains either through direct contact or through the food chain may lead to a serious therapeutic problem in human and veterinary medicine. Added to the particular structure of complex molecules such as veterinary and human pharmaceuticals, the research of the impact and behavior of these compounds in the nature represents great challenges for the future to come as scientists are just beginning to unravel the risks posed by their presence in the environment.
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In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 85-106 © 2011 Nova Science Publishers, Inc.
Chapter 4
MICROBIOLOGICAL SAFETY AND QUALITY OF ANIMAL FEEDING STUFFS
Marijana Sokolovic Croatian Veterinary Institute – Poultry Centre, Feed Analysis Laboratory, Heinzelova 55, 10000 Zagreb, Croatia
ABSTRACT
The quality of animal feedingstuffs has paramount importance for the welfare of animals. Contaminated feed is one of the potential sources of infections in animals. Besides causing of nutrient losses it can also cause detrimental effects on animal health and production. Many countries all over the world have implemented national and international programs of monitoring and control of raw and processed feeds for animals. These controls regularly include testing for general microbiological safety (by determination of total aerobic count, presence of pathogenic bacteria of genera Salmonella, Staphylococcus, Clostridium, pathogenic strains of E. coli as well as for other potentially pathogenic bacteria species). This control also includes testing for the presence of yeasts and pathogenic moulds. Although yeasts and some bacteria and moulds in feed can have protective effects, other can cause spoilage of animal feed. Furthermore, moulds can produce secondary metabolites called mycotoxins and they are usually present in cereal grains, the major ingredient of the animal feed. Therefore, potential presence of pathogenic moulds in feed results with reduction of the availability of nutrients and adds an additional health risk because of the presence of the secondary metabolites – mycotoxins. Usually, processes for feed production are efficient in destroying of majority of undesirable yeasts and bacteria. However, proper use of raw substances, production and storage conditions are important factors in assuring feed of adequate quality. The aim of this chapter is to summarize the current situation of microbiological safety of animal feedingstuffs, impact of mycotoxins and other undesirable substances on safety of animal feedingstuffs and animals, and significance of potential biological contamination of animal feedingstuffs and impact it has on food for human consumption as well as to give overview of recommendations concerning quality and safety of animal feedingstuffs. 86 Marijana Sokolovic
INTRODUCTION
Animal feed quality is an important factor in assuring the welfare and health of animals. Inadequate feed can cause adverse health effects on animals because most components are digested and absorbed during the passage through intestinal tract. Additionally, adverse effects can be a result of nutrient losses, and become visible by lowered production capabilities and emergence of diseases. Furthermore, since animals can transmit certain diseases to humans through contaminated food; special attention has been put in assessment of feeding-stuffs for food-producing animals. Therefore, we can consider feed as the beginning of the so called chain ―from stable to table‖. Feed in animal production is one of the keys for quality and safety of animal husbandry and has secondary implications in the food chain. It is a long time practice to use specific feeding regimes for each productive category of animals (production of animals for meat, milk, eggs, etc). In order to achieve better performances current practice is to include different feed additives that will also provide healthier life of animals. Additionally, special care has been focused on the environmental and other factors of animal production systems. However, the feed still remains a potential entrance of undesirable and potentially harmful substances that can cause diseases of animals and consequently of humans. Although harmful substances do not always cause visible symptoms of some diseases, in intensive production such intruders can easily be detected by decreased productive performance of animals. Therefore, assuring that animals receive quality and safe feed will along with other measures in animal production lead to healthier life and better productivity. Potential sources of risks of feed in animal production systems can be of chemical and biological nature. Chemical contaminants can be further classified into natural contaminants (e.g. mycotoxins, phytotoxins), environmental contaminants (e.g. dioxins and dioxin-like compounds, industrial pollutants like polychlorinated biphenyls and heavy metals) and authorised chemical products (e.g. residues of veterinary drugs and residues of herbicides and pesticides used for protection of crops) (Saegerman et al., 2006; Kan and Meijer, 2007). All of them can find its way not only into the organism of animals, but can also later end in food intended for human consumption. Biological sources of contamination of feed include a variety of bacteria, moulds and their metabolites that can cause diseases in animals, and as mentioned before, can end in the products intended for human consumption. Additionally, certain bacteria can induce zoonosis, diseases that can via various ways cause diseases in humans as well. Since most of them cause the diseases of alimentary tract, these types of diseases are usually called the alimentary diseases. The well known causes of alimentary infections are bacterial genera: Salmonella, Campylobacter, Listeria, Clostridium, Staphylococcus and bacteria E. coli.
Chemical Exposures
Although this chapter will focus on microbiology of feed, it is necessary to mention relevant information concerning potential chemical contaminants, including toxins in animal feed. The term contaminants in feed signifies the potentially harmful chemical substances, of anthropogenic or natural origin, that have not been intentionally added in feed, but present Microbiological Safety and Quality of Animal Feeding Stuffs 87 due to contamination during the various stages of its production, packaging, transport or storage, or because of deliberate treatment of accidental environmental contamination. Deliberate treatment includes the use of plant protection products, veterinary medicine products and feed and food additives (Saegerman et al., 2006; EC, 2008). It is generally considered that these contaminants, when present at certain levels in feed, have negative impact on the animal health and consequently can cause a risk to human health.
Natural Contaminants
As mentioned earlier in this chapter, some contaminants can be formed naturally (from activities of insects and microorganisms), or they are endogenous toxins, originated from specific primary and secondary substances produced by fodder plants (D'Mello, 2004). Their occurrence often depends upon geographical area and climate conditions. As a result of their presence in feed, they cause nutritional disbalances, reduction of reproductive and production performance and in the worst case scenario can impair health and cause death of animals. Since the extent of feed contamination due to insect fragments and excreta is generally not known, this type of contamination is usually neglected. However, they can have a potential role as a vectors in the transmission of fungal spores, toxins and even bacteria in feed (D'Mello, 2004). Much more data on occurrence and toxic effects is available for mycotoxins.
Phytotoxins
Phytotoxins are a product of microbial pathogens which often cause an obvious damage to plant tissue and cause characteristic symptoms of plan diseases. They are low molecular weight substances produced by some plant pathogens (fungi, bacteria) which are capable of reproducing symptoms similar to that found in natural infections in plants. Phytotoxins act directly on protoplast of the cell. Other pathogens such as high molecular weight polysaccharides secreted by wilt-inducing bacteria which obstruct the flow of fluid in the sylem vessels and may result in death of plant are not considered to be toxins (Amusa, 2006). They are classified according to their chemical properties (low molecular weight peptides, terpenoid structures, contain carbohydrates, structure) or based on the producing organism (fungi, bacteria) although more than one microorganism can produce the same phytotoxin. Further classification is based on biological activities (enzyme inhibitors, anti-metabolites, membrane-affecting compounds, etc) and on toxic selectivity to plant genotypes (host selective or non-host selective) and on role in disease development (Wheeler and Luke 1963; Amusa, 2006). Therefore, phytotoxic metabolites have been found associated with bacteria and fungal pathogens, which cause symptoms similar to those caused by the originating pathogen. All these pathogens and its implications in plant disease management are reviewed by Amusa (2006) and include: pinolidoxin produced by Ascochyta pinodes, deoxyradicin and maculosin grom Alternaria helianthi and Alternaria alternata; piricularin from Piricularia oryzea; vicotrin (victorin) form Cochliobolus vitoriae; phaseolotoxin from Pseudomonas syringae pv. Phaseolicola; toxin from Periconia circanata; saccharitoxin from Helmithosporium sacchari; cercosporin produced by the species Cercospora; colletotin, colletotrichin and colletopyrone produced by Colletotrichum spp. Several fungal phytotoxins 88 Marijana Sokolovic have also been produced in „in vitro―conditions, among which the most important are: toxins from Mycosphae rella fijiensis and Mycosphaerella musicola; phytotoxins produced by Fusarium species including: fumonisins, moniliformin, fusaric acid, 2,5-anhydro-Dglucitol (AhG) and trichothecenes (Abbas et al., 1991; Abbas and Boyette, 1992; Jin et al., 1996; Amusa, 2006). The most characterized phytotoxins include Abrin, produced by Abrus precatorius, Ricin produced by Ricinus communis and Crotin produced by the plant Croton tiglium. Additionally, new molecular methods have enabled detection of endofungal bacteria as producers of mycotoxins. Mycotoxins such as Rhizotoxin and Rhizonin are not synthesized by the fungus itself but by bacteria residing within the fungal cytosol. Both toxins are potent toxins for plants and animals (Lackner et al., 2009). Animal toxicity of phytotopathogenic microorganisms is usually associated with mycotoxins. For many fungal species it has been demonstrated to possess animal toxicity potential (Main and Hamilton, 1972). However, direct relationship between phytotoxic metabolites and their animal toxin potential is still the theme of many current researches. A large group of toxins found in plants and associated with disease in humans and animals are Pyrrolizidine Alkaloids (PA) (Prakash et al., 1999). Toxic alkaloids are produced by all the genera of the family Boraginaceae, genera Senecionae and Eupatoriae that belong to the family Compositae, and genus Crotolaria, a member of the family Leguminosae. Toxic Pyrrolizidine Alkaloids can be present in milk from cows and goats, in medicinal plants (comfrey), in honey and hen‘s eggs (Prakash et al., 1999; ANZFA, 2001). Diseases caused by Pyrrolizidine Alkaloids include diseases caused by Senecio spp. like Seneciosis (Winton Disease, Molteno Diseases, Pictou Disease and Walking Disease of horses), diseases caused by Crotolaria spp. in cattle and horses called Missouri River Bottom Disease and Jaagseikte) and by Heliotropium europaeum and Echium plantagineum in sheep and cattle. As stated earlier, the food for human consumption that has been found to contain PAs includes grains, honey, milk, offal and eggs and consequently present potential threat for human health. There is also a lack of data of occurrence of residues of PA in meat (ANZFA, 2001). Therefore, presence of such substances in animal feed could potentially present of threat to humans if certain quantities are found in products of food-producing animals. The exact relationship still has to be evidenced in comparison to levels of PAs in various food and feed. Current interest in phytotoxins from microorganisms is in their use as herbicide instead of using living organisms. However, use of a potent phytotoxins in plant disease control will not be efficient unless the virulence and the biology of the target host plant/weed and the pathogens are understood.
Mycotoxins
Mycotoxins are low molecular weight secondary metabolites produced by moulds. The most common strains of filamentous fungi are genera Aspergillus, Penicillium and Fusarium. Its occurence depends upon geographical area and climate conditions (temperature and humidity). Generally, almost 25% of all grains all around a world are every year contaminated with mycotoxins (FAO, 1998). According to occurrence data and their pathogenic potential, the most important toxins are: aflatoxins, T-2 toxin, fumonisin, zearaleone, ochratoxin and deoxynivalenol (vomitoxin). Since some of them can be detected in eggs (aflatoxins), milk (aflatoxin), animal liver, muscle, kidney (aflatoxin, ochratoxin A, Microbiological Safety and Quality of Animal Feeding Stuffs 89 zearalenone), they can consequently negatively influence the health of humans consuming this type of food (FAO, 1998). Aflatoxins are produced by Aspergillus species (A. flavus, A. parasiticus, A. nomius and A. pseudotamarii (Kurtzman et al., 1987; Payne 1998; Ito et al., 2001; Cortyl, 2008). Its negative effects have been documented in different animal species and in humans. Generally, younger animals are more susceptible, but in all animals, aflatoxin can induce disease characterised by damage of liver and gastrointestinal tract, reduced reproductive and productive performance, reduced feed intake and impairment of immunity (Leeson, 1995). Zearalenone is non-steroidal estrogenic mycotoxins produced mainly by Fusarium graminearum. Its negative function is evident in interference with ovulation, conception, implantation and foetal development, increased incidence of abortions and other reproductive problems in animals (mostly pigs). Thus, this toxin imitates the function of the female oestrogen hormone (Minervini and Dell‘Aquila, 2008). Similar symptoms have been detected in other species. On the other hand, poultry, is considered relatively resistant to this toxin. Ochratoxins are secondary metabolites of mainly Aspergillus ochraceus and Penicillium verrucosum (Wood, 1992). According to its negative function on host organism, ochratoxin is a nephrotoxin and can cause damage of liver, gastrointestinal tract and lymphoid tissue (Akande, 2006; Cortyl, 2008). These effects have been documented in various animal species including pigs and poultry. Trichothecenes are a vast group of toxins with similar structure, produced manly by the species from the genera Fusarium, although it has been documented to be produced by other genera of filamentous fungi including Stachybotrys, Trichoderma, Myrothecium, etc. According to their chemical properties they are divided to macrocyclic (satratoxins, roridin and verrucarin) and non-macrocyclic groups. Non-macrocyclic group is further divided in Type A (T-2 toxin, HT-2 toxin, diacetoxyscirpenol) and Type B trichothecenes (deoxynivalenol, nivalenol and fusarenon X) (Bata et al., 1985; EC, 2001; Ueno, 1977). Among all these toxins, the most often detected trichothecene in feed is deoxynivalenol, while T-2 toxin is the most potent Type A toxin that is also listed in the group of biological weapons. Toxic effects of T-2 toxin include inhibition of protein, DNA and RNA synthesis, cytotoxicity, immunomodulation, lesions in digestive tract, damages of organs and skin, neural disturbances and reduced productive performance of affected animals. It can cause toxic effects in various animal species and it has been implicated in several outbreaks of human mycotoxisis. Deoxynivalenol, a representative of Type B trichothecenes, can cause loos of productive performance, reproductive problems, impairment of digestive and immune system. Its toxic effects are most pronounced in pigs, and to less extent in poultry (Leeson, 1995). Fumonisins are toxic compounds produced mainly by Fusarium moniliforme. Its toxic effects include impairment of immune system, respiratory problems, damages of liver, kidney damage and reduced productive performances of food-producing animals. Generally, mycotoxins cause mycotoxicoses and their toxicity depends upon various factors including administration route, duration and number of exposures, dose, animal's age, sex and overall health as well as presence of other mycotoxins (JECFA. 2001: WHO, 1990; WHO, 2002: Sokolovic et al., 2008). Humans are intoxicated from the same sources as animals (contaminated food) and occurrence of residues in product of animal origin is important global problem. There is no adequate and successful treatment of ill animals or humans and prevention is the only way for reduction of entrance of these harmful substances 90 Marijana Sokolovic in food chain. Prevention of intoxication can be achieved by regular monitoring programs at different stages of food and feed production.
Environmental Contaminants
As stated earlier in this chapter, feed products should not contain chemical contaminants. Since the current societies have used, or still are using a variety of chemicals in everyday life, it is inevitable consequence that food and feed contains residual levels of different chemical. To achieve safe feed and food, countries have developed monitoring programs for those chemicals that are considered the most important based on the current research findings and risk assessment. Environmental contaminants are usually result of industrialisation and/or urbanisation (dioxins and dioxin-like compounds, polychlorinated biphenyls, heavy metals) and a result of use authorised chemical products such as veterinary drugs, herbicides and pesticides (Saegerman, 2006). All of them can contaminate feed and food in each step of production, transport, storage and final use. Although the first group of these contaminants is not the subject of this chapter, it is necessary to mention them to create a complete picture of potential hazards in feed. Importance of most of these chemical contaminants has been determined after deliberate or not-deliberate use in production systems of food-producing animals. To name just a few, dioxins and dioxin-like compounds and polychlorinated biphenyls (PCB) have been detected in feed for poultry, swine and ruminants (cattle, cheep and goats) in several European Countries. These feeds were fed to animals, and consequently the animals became contaminated with dioxins and PCBs. If some products of these contaminated animals have been used to manufacture human or animal drugs or biological products, the final product, intended for humans would also be contaminated. Sources for contamination of animal feedstuffs are fish meal, fish oil, animal fat, certain plant materials and animal by-products (milk by-products, meat and bone meal) and roughages. According to the Opinion of the Scientific Committee on Animal Nutrition on the dioxin contamination of feedingstuffs and their contribution to the contamination of food of animal origin (EC, 2000), it has been estimated that adverse effects from dioxins would be expected in animals challenged by severe accidental contamination with dioxins or PCBs and not in animals exposed to the current levels of background pollution. In order to avoid such contamination, it is necessary to apply an integrated approach that encompass proper monitoring, further research of potential carry-over of toxic substances in food products and implementation on quality principles in feed and food production. Other chemical contaminants that present a potential risk for animal and human health and have been detected in food and feed include melamine and heavy metals like cadmium, lead, mercury and arsenic (Khan et al, 2000: Saegerman et al., 2006; WHO, 2008; WHO, 2009). Authorised chemical products, including veterinary drugs, herbicides, pesticides and hormones have also been found in trace levels in different feed. The use of most of these substances is currently forbidden in various countries. Residues of veterinary drugs include antibacterial and antiparasitic drugs that can cause direct toxicity, induce allergic reactions, promote development of antibacterial-resistance in bacterial strains and interfere with starter Microbiological Safety and Quality of Animal Feeding Stuffs 91 cultures for production of fermented food products (Donoghue, 2003; Acar et al., 2006; Saegerman et al., 2006). Antibiotics have been used in animal industry to enhance the health and productivity. The problem of antimicrobial resistance arose because of overuse of antibiotics for treatment of diseases in humans and because certain antimicrobials used for treatment or for growth promotion in agriculture have also been used for disease control in humans. Today, the use of antimicrobials is strictly regulated in many countries in order to prevent their unsafe use and to avoid consequent reduced efficacy. Despite of the benefit of their use, an important issue in the use of antibiotics in food producing animals is the development of bacterial strains that are resistant to one more antibiotics. Furthermore, this resistance can be transmitted to other bacterial strains that can also cause the diseases of humans (and animals). Resistant bacteria can be transferred from animals to humans through food (meat contaminated with antibiotic- resistant bacteria), through direct contact with animals (handling of animals, feed and manure) or through the environment (contaminated water and soil). Diseases of humans caused by resistant bacteria and linked to the agricultural overuse of antibiotics include food poisoning with bacterial genera Salmonella, Campylobacter, Enterococcus and E. coli. In order to prevent spreading of resistance genes among pathogens a complex approach of banning antibiotics in animal industry, monitoring programs and application of quality and safety rules has been adopted in many countries (WHO, 1997; Lipsitch et al., 2002; van den Eede et al., 2004; FDA, 2010). Horizontal gene transfer is a natural process and an integral part of microbial life. However, overuse of antibiotics and growth promotors for farm animals has contributed to the spread of genes responsible for resistance to certain antibiotics. For example, antibiotic resistance of these bacteria limits the therapeutic options available to veterinarians and physicians for the certain cases of non-typhoid Salmonella which require treatment. Example of such multi-resistance in bacteria is a strain S. Typhimurium DT104, which has become resistant to ampicillin, tetracycline, streptomycin, chloramphenicol and sulphonamides and is prevalent in some countries. After introduction of fluoroquinolones for use in food-producing animals, certain serotypes of Salmonella and strain Campylobacter jejuni that have reduced susceptibility or are resistant to these group of antimicrobials have been isolated from animals and humans (Lipsitch et al., 2002; Smith et al., 2002; INFOSAN, 2005). To determine the significance of the specific microorganism and/or the specific type of resistance, microbial risk assessment has been applied to evaluate the level of exposure and the subsequent risk to human health. This can be achieved after detailed study of current situation (prevalence) in the whole world. Therefore, an international program is necessary to monitor antimicrobial resistance in food animals and food of animal origin. Such program will contribute to the detection and prevention of transmission of resistant bacteria and resistance determinants from animals to humans and the clever use of antimicrobials in animals and humans (WHO, 1997; Snary et al., 2004). Pesticides are chemical substance or a mixture of it used for preventing, destroying, repelling and or mitigating any pest (insects, rodents, weeds, fungi, microorganisms) that can damage crops and cause loss of productivity in livestock industry. The most common pesticides are insecticides, herbicides, rodenticides and fungicides. The origin of pesticides in animal feed is mainly due to fact that most foods of plant origin are grown using pesticides in order to increase agricultural productivity. Detrimental effects of pesticide residues have been evidenced both in human and animals (Kan and Meijer, 2007; Khaniki, 2007). It has been 92 Marijana Sokolovic estimated that pesticide residues are often found in animals feed, and after feeding, these substances have accumulated in tissues with higher fat content, such as adipose tissue, brain, liver, kidney and milk. Consequently, animal products such as meat, milk and butter are contaminated with pesticide residues what present a threat for human health (Kan and Meijer, 2007; MacLachlan and Bhula, 2007).
BIOLOGICAL EXPOSURES
Some of the microorganisms are naturally present during the growth of the plans; consequently they are usually also present in feed. The growth of pathogenic microorganisms in feed is the cause of feed-borne diseases in animals. Some of them result in transmission of pathogens in animal products intended for human consumption, what lead to potential infection in humans. In animals, feed is not the only source of diseases, but is an important factor that can contribute to it. In a case of food-borne zoonotic microorganisms, they can also contaminate the feed through faeces. A practice of use of animal manure in animal productions is therefore a potential risk of such infections. Among pathogenic bacteria that can be transmitted through the feed, the most important bacterial strains belong to the genera Salmonella, Campylobacter, Listeria, Clostridium and E. coli, while other genera like Staphylococcus and Streptococcus are of less importance. Other microbiological risk includes contamination with fungi (mainly genera Aspergillus, Penicillium and Fusarium) and mycotoxins which have been mentioned earlier in this chapter. Importance of other potential hazards in feed caused by parasites, viruses and prions are not considered in this chapter.
Bacteria
Salmonella
Salmonella are rod-shaped, gram-negative, non-spore forming and predominantly motile bacteria, a member of the family Enterobacteriaceae. Genus Salmonella, and especially S. Enterica (predominant serovars Enteritidis, Typhimurium, Virchow and Infantis) has emerged as a leading cause of human infections in many countries (after Campylobacter spp.). It is one of the most common and widely distributed pathogen that can cause foodborne diseases in humans and animals (Morales and Thurman, 1993). The main source of Salmonella diseases in humans are hen eggs and swine and poultry meat. In animals, contaminated feed is one of the main sources of infection with Salmonella. Other routes of exposure include transmission through environment (faeces, rodents, etc). Some animal species does not always show signs of clinical illness, but because bacteria can colonize the intestinal tract, can be carriers of pathogens which can enter the food production chain (Foley et al., 2008). There are certain evidence that specific Salmonella strains present in feed ingredients has passed through the production process and were found in the finished product (MacKenzie and Bains, 1976; Anderson et al., 1997). Certain bacterial strains cause Microbiological Safety and Quality of Animal Feeding Stuffs 93 foodborne gastrointestinal infections in different animal species. However, such infections are most common in poultry resulting in production of contaminated eggs and meat. Salmonella strains can be eliminated from feed by heat treatment and addition of organic acids, as well as its combination (Vanderwal, 1979; Hinton and Linton, 1988; Beal et al., 2002). Prevention of disease in food-producing animals can be achieved by applying principles of good farming practices and controlling and preventing of entrance of undesirable substances in the production chain (Sauli et al., 2005; WHO, 2006).
Campylobacter
The genus Campylobacter comprises sixteen species and six subspecies of S-shaped or spiral shaped, gram-negative, non-spore-forming bacteria with single polar flagella at one or both ends that is responsible for a characteristic corkscrew-like motility. They usually grow under microareophillic conditions and some strains (like C. jejuni, C. coli and C. lari) are thermophilic and grow optimally at 42°C and are unable to grow below 30°C. Although their growth is pretty fastidious, they lack many adaptive responses which are correlated with resistance patterns in other bacteria and are remarkably sensitive to environmental conditions (temperature, atmospheric concentrations of oxygen, free radicals, peroxides and desiccation); they are one of the main causes of human bacterial intestinal diseases (Griffiths and Park, 1990; Parkhill et al., 2000). It is also worth to mention that even though Campylobacter is unable to grow at low temperatures, it can still survive in unfavourable environments and can be isolated even from frozen products (Fernandez and Pison, 1996; Hazeleger et al., 1998; Parkhill et al., 2000, Stead and Park, 2000). Among all species, only C. jejuni and C. coli are considered as food safety issue. In humans, these bacteria cause bacterial diarrhoea and post- infection immune-mediated neuropathies (Ang et al., 2001). Source of infection in humans is usually handling and consumption of raw poultry, while other reservoirs such as vegetables and dairy products (meat and milk) are also evidenced (Stanley and Jones, 2003; Horrocks, 2009). However, since the high potential of spreading of bacteria inside of a broiler flock (100% of birds may be colonized within a few days) and the fact that poultry has a core temperature of 42 °C (optimal growth temperature of Campylobacter) poultry is frequently associated with illness in humans (Petersen et al., 2001: Corry and Abatay, 2001; Nevell and Fearnely, 2003; Stanley and Jones, 2003). While it is well documented that major reservoirs of thermophilic Campylobacter for humans are the intestines of warm-blooded animals (incidence of bacteria in intestines usually exceed 80%), the route of infection of animals is usually related to transmission from animals that are shedding high numbers of the organisms (shedding greater than 105 organisms per gram). Such animal carriers can contaminate water, pasture and other feed which than act as vehicles for horizontal transmission among animals. Other sources of infection of animals include vertical transmission from parent flocks, carry-over from a previous flock, contacts with domestic and wild animals, feed withdrawal and environment. Important epidemiological feature of Campylobacter infection is that it is not usually found in young animals (Cox et al., 2001; OIE, 2008). Infectious dose is rather low (less than 500 bacterial cells) and even 10 cells can cause an infection in humans (Riordan et al., 1993). In animals, there are usually no visible signs of infection, but without control they can easily spread and present a threat for human health (OIE, 2008). Since Campylobacter are frequently found in 94 Marijana Sokolovic the intestine of poultry, cattle, sheep and pigs, improper handling during slaughter often leads to contamination of meat with this pathogen. This is the reason, why many countries have included Campylobacter in their monitoring programs of potential zoonotic pathogens. Preventive measures include the common cleaning and preparation practices in food- producing industry (application of acid chemical sprays, irradiation methods, pasteurisation and post-chilling methods), increased processing hygiene practice on farms (biosecurity measures for prevention of contamination and entrance of pathogen on farm) and application of safe handling procedures in households (rinsing and cooking practice) (Griffiths and Park, 1990; Ge et al., 2006; Horrocks et al., 2009).
Listeria
The bacteria from the genus Listeria are facultative intracellular organisms that can replicate in the cytoplasm of phagocytic cells due to expression of listeriolysin O toxin. The genus Listeria includes six species L. monocytogenes, innocua, L. welshimeri, L. seeligeri, L. ivanovii and L. grayi, but most infections are caused by the most prominent representative of the genus is L. monocytogenes (McLauchlin et al, 2004). This is a rod-shaped, gram-positive, non-spore forming, aerobic or facultative-anaerobic bacteria. This pathogen is motile via a few peritrichous flagella if grown at temperature below 30°C (OIE, 2008). The bacteria can survive for a long time in unfavourable environmental conditions including low temperatures and many food producing processes (Novak et al., 2003: FAO/WHO, 2004; Gao et al, 2006, 2007). It can infect various animal species and humans by causing clinical or subclinical type of diseases. In animals, the clinical disease is usually only evidenced in ruminants (cattle, sheep and goats) with the symptoms of encephalitis, septicaemia, mastitis and abortion. Other animal species are usually carriers of diseases without showing any clinical signs. The sources of infection in animals include environment, feed, especially forages (silage with high pH), because it is considered that this pathogen is not able to replicate at pH below five (Fenlon, 1985; Shoder et al., 2003; Wagner et al., 2005). The pathogen can also enter the organism through the nasal mucosa, conjuctiva and wounds. Contamination of feed is usually through contaminated faeces of asymptomatic animal carriers. Consequent contamination of milk can be result of faecal contamination during milking or a result of mastitis, while contamination of meat usually occurs during slaughtering of animals. As a result, animal products, consumed by humans can cause a serious disease called listeriosis (Posafy-Barbe and Wald, 2004). The disease may be sporadic, but it has high fatality rates (20-30%) and person that have increased susceptibility are predominantly infected (FAO/WHO, 2004). Therefore, in humans, these bacteria also cause foodborne disease, of which main sources of infection are food of animal origin (Low and Donachie, 1997; FAO/WHO, 2004; McLaughlin et al., 2004). Prevention of diseases can be done in the same way as for other foodborne pathogens and include regular monitoring or potential hazards as well as implementation of good farming principles in production.
Microbiological Safety and Quality of Animal Feeding Stuffs 95
E. coli
Escherichia coli are ubiquitous gram-negative commensal bacteria that inhibit the lower intestinal tract of animals and humans. However, certain serotypes are considered pathogenic for humans and animals because of determination of pathogenic characteristics such as production of verocytotoxins and shiga toxins and causing diseases in humans and animals with severe clinical symptoms (Winfield and Groisman, 2003). The bacteria have the ability to respond to new selection pressures and to exploit new environments because of the evolution of bacterial genomes. Namely, horizontal genetic transfer has been related to this fast adaptation of bacteria to various conditions. For example, transfer of DNA fragments containing the genes responsible for virulence characteristics and antibiotic resistance permits E. coli to survive in competitive environments (Lawrence and Ochman, 1998). Most of these relationships have been elucidated after completion of sequence of E. coli genomes (Blattner et al., 1997). Currently, there are more than 700 serotypes of E. coli which can be distinguished by ―O‖ and ―H‖ antigens and flagella. One of the most pathogenic representatives of these bacteria is the shiga-toxin producing E. coli O157:H7 strain. It has been linked to large outbreaks of gastrointestinal diseases in humans (diarrhoea, hemorrhagic colitis and haemolytic-uraemia syndrome) and some animals (Manning et al., 2008). Potential sources of infection with pathogenic E. coli O157:H7 are contaminated meat, water, milk, fruits and vegetables. Evidenced were also transmissions of E. coli through the contact with animals and humans. However, the main source of infection in humans is contaminated cattle meat and some vegetables such as lettuce, cabbage, parsley and spinach (Keene et al., 1997; CDC 2006a, CDC 2006b; CDC 2009) although any food or beverage contaminated by animal (especially cattle) manure can after consumption result as a disease (Mead et al., 1999; Elder et al., 2000; Keen et al., 2003). However, cattle are considered as a reservoir of these pathogenic strains (Belongia et al., 1991: Chapman et al., 1993; Wells et al., 1991; Chapman et al., 1997; Manning et al., 2008). Additionally, pathogenic E. coli O157 have also been isolated from other animal species such as pigs, sheep, horses, deer, dogs and poultry in which this pathogen can either cause a similar disease (porcine oedema disease, HUS-like disease in dogs, disease in horse) or cause shedding of microorganism (cattle, deer, pigs and poultry) (Hafez and Löhren, 1990; Whipp et al., 1994; Hammermueller et al., 1995; Chalmers et al., 1997; Chapman et al., 1997; Fischer et al., 2001; Booher et al., 2002; Feder et al., 2003; Kabir, 2010). Presently, there is only scarce information on the prevalence of E. coli O157 in feed and food animals other than cattle. Even in feed for cattle, the source is not clear. According to some authors it depends upon the type of the diet (grains or hay-feed). However, it is not the fact that all or most of human disease associated with E. coli O157:H7 can be attributed to feeding cattle grain instead of hay (Callaway et al., 2009) It is not documented to what extent does animal feed presents a potential route of contamination of food for humans, although it is a source of transmission of pathogenic strains in animals (Rasmussen and Casey, 2001). There is also certain evidence that some probiotic bacteria are efficient at reducing the level of carriage of E. coli O157:H7 by cattle (Zhao et al., 1998). Since the rather small number of bacteria can cause a serious illness in humans for which there is no fail-safe therapy and that E. coli bacteria can survive for weeks on different surfaces it is necessary to include these pathogens in monitoring programs as well as 96 Marijana Sokolovic investigate the potential of feed in the outbreak of disease in humans and animals and that food-producing industry apply and implement preventive effective measures.
Other Potentially Pathogenic Bacteria
These mainly include bacteria that belong to the genera Clostridium, Staphylococcus and Streptococcus. Since the disease outbreaks are rather sporadic, contaminated feed can be a source of animal infection (mainly as a vector), and animals can act as a route of sporadic infections in humans. The genus Clostridium is a large group of gram-positive, rod-shaped bacteria that form endospores and have strictly fermentative type of metabolism. Most of them grow under anaerobic conditions, but spores can also survive for a long time even if exposed to air (Todar, 2005). Not all of them are pathogenic for humans or animals. Pathogenic species such as C. perfringens, C. difficile, C. tetani, C. sordelli and C. botulinum are actually opportunistic pathogens. The later two species also produce strong toxins that negatively affect animals and humans. C. perfringens is considered to be an important cause of food poisoning usually because of the production of enterotoxin (alpha, beta, epsilon and iota lethal toxins). Pathogen is often correlated with onset of diarrhoea of humans and animals. Other important food-borne pathogen from this genus is C. botulinum which causes neuro- intoxication with clostridial toxin with similar poisoning as with staphylococcal toxin. Diseases is usually fatal, although fortunately rather rare and the incidence of its spores is rather low in the environment. Since feed and food can be a source of clostridial infections and intoxications, it is of paramount importance to practice proper food and feed handling and preparation (Peck, 2006; EC 2006). For prevention of infections with C. perfingens special attention should be given to handling of meat since the bacteria can be easily found in the animal intestines and contamination of animal food products can occur during slaughter process (EFSA, 2004; EC 2006). Implementation of quality system in food production and strict control that includes specific cooling and storage temperatures can prevent food poisoning in majority of cases. There are some reports that food may be contaminated with Gram-positive, coccoid meticillin resistant Staphylococcus aureus (MRSA) which means that eating and handling of contaminated food and feed is a potential vehicle for transmission (EFSA, 2009). Although Staphylococcus aureus is normal inhabitant of respiratory and gastrointestinal tract of humans and animals and environment, some strains can cause a disease especially if the routes of entrance are damaged skin and wounds. The importance of MRSA is in his resistance to almost all ß-lactamase resistant penicillins of the meticillin family antibiotics (such as oxacillin, cloxacillin and meticillin), cephalosporins and carbapenems and often to aminoglycosides, macrolides, and fluoroqinolones, what result in a serious problem for treatment of disease in humans (Lee, 2003: EFSA, 2009). Additionally, these resistant strains are considered as a result of antibiotic use in farm animals. Besides livestock, companion animals and horses can also be infected with MRSA either from contact with humans, other animals, through the environment by indirect contact with contaminated surfaces or by the airborne route and consequently be a reservoir for humans and animals (Lee, 2003; Asoh et al., 2005; EFSA, 2009). The primary reservoirs of MRSA for humans are usually pigs, calves and poultry (broilers) while other sources are estimated as sporadic. The disease is a result of Microbiological Safety and Quality of Animal Feeding Stuffs 97 consumption of heat-resistant enterotoxins produced by S. aureus and symptoms include vomiting and diarrhoea (EFSA 2009). In animals, presence of this pathogen is usually associated with asymptomatic carriage in food-producing animals. However, the incidence in animals is relatively low but can result in serious disease (suppurative disease, mastitis, arthritis and urinary tract infection) in poultry, dogs, cats, cattle and horses (Lee, 2003; Persoons et al., 2009). The methods for eliminating the potential risk are similar to those for other pathogens and include monitoring and identification. While this approach has been applied in humans, occurrence of MRSA is usually not monitored in animals. Additionally, hygiene measures should be applied not only among humans but also in contact with animals. The genus Streptococcus comprises a number of strains of Gram-positive, microae- rophilic non motile coccoid non-sporulating bacteria. Since the strains generate lactic acid as a major end product of their fermentative metabolism they belong to a group of so called lactic acid bacteria (other genera of this group include Enterotoccus, Lactococcus, Lactobacillus and Leuconostoc) which are important in the production of fermented foods. They are classified by a combination of antigenic, haemolytic and physiological characteristic in seven groups (i.e. A, B, C, D, F and G). Some strains belong to normal flora of oral cavity or intestinal tract of humans and animals. Among them only strains from groups A (40 antigenic types) and D (classified as the genus Enterococcus) are important as food pathogens. The most prominent representative of the group A is Streptococcus pyogenes, while E. faecalis and E. faecium are important food poisoning pathogens from the group D (Hardie and Whiley, 1995). Many species of streptococci are responsible for diseases in animals. In short, S. agalactie, S. uberis, S. parauberss, S. dysgalactie and S. canis are most common causative agent of bovine mastitis. Infections in pigs are characterised by meningoencephaliltis, arthritis, endocarditis, pneumonia and septicaemia caused by S. porcinus and S. suis. In horses, S. equi causes pharyngeal and submaxillary abscesses. In sheep, S. dysgalactie causes arthritis, bacteraemia, endocarditis and mastitis. Other animal‘s species including poultry are usually not infected with streptococci (Hardie and Whiley, 1995). Streptococcus pyogenes (A group) is one of the most frequently found pathogen in humans and can be cause of puerperal fever, pharyngitis, erysipelas, and other invasive infections (Todar, 2005). Foodborne pathogenicity of these bacteria is uncertain, but since it can be transmitted through contact with carriers, have been isolated from food products and bacteria of this genus can survive under unfavourable conditions for a long time, food can serve as a secondary source of this pathogen in humans (van Gerwen et al., 1997). Enterococci, formerly a D group of the genus Streptococcus, are the member of autochthonous bacteria of mammalian gastrointestinal tract, and not often found in livestock (Leclerc et al., 1996). However they have been isolated from faeces of calves and raw milk (Devriese and Pot, 1995). In humans, enterococci are considered as emerging pathogens in community-acquired infections including endocarditis, bacteraemia, and infections of urinary tract, central nervous system and intra-abdominal infections (Murray, 1990; Morrison et al., 1997). Sources of infection in humans include patient‘s endogenous mikroflora, person-to- person transmission and meat of farm animals (cattle, pigs and poultry) in which there has been ergotropic use of avoparcin (Gordts et al., 1995; Bager et al., 1997; Franz et al, 1999). However, it is still not well established in what extent food contributes to the illness in humans. But, because of the high thermotolerance of these bacteria, they can be a spoilage problem of meat because of potential contamination from intestinal or environmental sources 98 Marijana Sokolovic
(Franz et al., 1999). Therefore, although sporadic, these bacteria need to be considered as potential opportunistic human pathogens.
Potentially Pathogenic Moulds
Filamentous fungi, members of the genera Aspergillus, Penicillium and Fusarium, as well as yeasts Candida spp. are generally considered as pathogenic threats for human and animal health. Recently, invasive fungal infections have become a common cause of infections of immunocompromised individuals with a high rate of morbidity and mortality (Tell, 2005). Infection is caused by Aspergillus fumigatus strains, and less often by other Aspergillus species, such as A. niger, A. microsporum, A. terreus. Disease has also been known in avian species. The most studied species of fungi are those that cause diseases in humans. Those diseases are usually sporadic and are evidenced in immune-compromised individuals. In animals, the most significant effects of moulds are the production of secondary metabolites, mycotoxins that can result in diseases called mycotoxicosis. Among pathogenic moulds, Aspergillus species have been evidenced as the cause of diseases called Aspergillosis. The disease has been documented in dogs, equines and birds (turkeys, waterfowl). The route of exposure is the inhalation and Aspergillus spp. usually colonize the lower respiratory tract. The most common species that cause avian aspergillosis are A. fumigates and A. flavus. Clinically, the symptoms are non-specific (inappetence, anorexia, lethargy) or can be related to symptoms of respiratory system dysfunction (rhinitis, dyspnoea). Feed can be a route of exposure if it is contaminated with aforementioned fungi in significant extent. However, inhalation of contaminated feed or from environment is predominant route. Treatment of diseases are still a problem in veterinary and human medicine and current known medicines only solve certain symptoms, while the complete recovery is hardly ever achieved. Since the grains are commonly the most contaminated with these moulds, if such ingredients are used for human consumption, they can cause illness in humans in the same way as in animals. Therefore, regular monitoring programs and prevention of entrance of contaminated feed is potential way of reducing the number of illness in both, animals and humans.
CONCLUSION
In this chapter, a concise review of current hazards of feed in the food-producing animals is presented as well as potential sources of human diseases and exposure. Prevention of chemical contamination of animal feed and minimising the biological exposure in food- producing animals is the current objective of animal producers. In order to achieve that, they have to implement the quality and safety principles that are known as the hazard analysis and critical control point (HACCP) system. Many countries all over the world have implemented national and international programs of monitoring and control of raw and processed feeds for animals to obtain relevant data of actual hazards in animal production and to increase the awareness of animal producers of Microbiological Safety and Quality of Animal Feeding Stuffs 99 potential sources of contamination and diseases of humans and animals which all start with the inadequate animal feed. Considerable effort has been put in application of microbiological and chemical risk assessment of feed in relation to health and welfare of animals, as well as potential risk of food products for humans. For proper assessment it is necessary to collect the incidence data, dose-response relationships of effects in animals and humans and the routes of exposure. However, current situation does not have to be constant, and continual monitoring and research of aforementioned area must be also be a part of the future objectives in animal husbandry and protection of humans. And the main advice for assuring safe feed and food is to implement the good manufacturing principles as the everyday routine.
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Chapter 5
EFFECTS OF SELECTED FEED COMPOUNDS AND FEED ADDITIVES ON GASTROINTESTINAL TRACT FUNCTIONS IN FARM ANIMALS: HEALTH VS. PRODUCTIVITY PERSPECTIVE
Violetta Naughton and Patrick J. Naughton Northern Ireland Centre for Food and Health (NICHE) School of Biomedical Sciences, University of Ulster, Ireland
ABSTRACT
The composition of diets for farm animals, as well as their supplementation with different feed additives in order to obtain better performance, has evolved over past decades. However, the performance response to many feed compounds and additives, especially in growing animals, is often variable. Specific interactions between dietary components and the gastrointestinal tract (GI) functions can help us to understand the sources of this variability. Furthermore, a physiological approach towards animal diets may assist in formulation of the modern diets that on the one hand will assure animals‘ health but also will be financially viable. Thus the primary objective of this report is present the influence of selected feed compounds and feed additives on GI tract homeostasis. Specifically, to demonstrate the relationship between specific dietary protein and peptides, selected feed additives including feed antibiotics, amino acids, and feed acidifiers, as well as selected secondary plant products (plant lectins) on physiological functions of the gastrointestinal tract (including GI motility, gastric and pancreatic secretion, intestinal absorption, GI microflora) in animals, particularly during their growth and development.
INTRODUCTION
United Nations Department of Economic and Social Affairs (UN report, 2004) predicts that by 2050 the overall world population will increase by medium 2.8 billions (within a minimum and maximum of 1.3 and 4.5 billions respectively) from the current 6.1 billion 108 Violetta Naughton and Patrick J. Naughton people. Although, the predicted growth rate is not as substantial as the growth observed over last few decades (for comparison over last 40 years the world population nearly doubled), however, even this relatively smaller growth will substantially increase overall pressure on global food production, especially primary production i.e. agriculture. The pressure on primary food production may be exacerbated by the demand for high value-added food i.e. the outcomes of the animal production. For example, over the last two decades the worldwide meat consumption increased by approximately 65% and further growth is forecast, and similar growing global demand applies to milk production (Delgado, 2003). The increasing demand for high-value added food is related to the economic growth of many developing countries, in particular in Asia and Africa (Delgado, 2003), which also show the highest predicted values for population growth. It may appear that an increase in primary food production may be achieved by increases in arable land and pasture, as it is estimated that approximately 27% of worldwide land mass can qualify for primary food production, while currently less than half is used for agricultural purpose. However, the predicted increase in world population will concomitantly increase the demand for a land required for other human activities such as building sites especially for housing but also industry (Vitousek et al. 1997). Furthermore, the developing new technologies related to the biofuels production may compete with primary food production for suitable land. Additionally, some land has to be made available for natural environment and conservation of wildlife so as to maintain biodiversity of particular habitats and ecosystems. All in all, the future availability of additional arable land to meet increasing global food requirements is uncertain. Therefore, it appears that one unit of land will have to sustain increasing numbers of people. Indeed, it is forecast that in a few decades one hectare of land will have to produce enough of food to sustain up to five people, while by comparison in 1960 one hectare of land had had sustained only two people. Primary food production depends on the natural environment, and not only on suitable soil but equally the availability of water. For example, approximately 1350 cubic meters of water are required to produce one metric tone of a cereal plant, such as wheat. The water requirements are even higher in production of high value-added food e.g. approximately 11000 cubic meters of water are needed to produce 1 metric tone of bovine carcasses (Hoekstra and Chapagain, 2008). Yet again the animal production demands are most prominent when compared to any other type food production. Paradoxically, in western countries animal food production is under increasing socio- economic pressure. On the one hand there is the expectation that the food produced (e.g meat or milk) will be cheap, while on the other hand there is the expectation of a product, which is of very high quality and safety, and obtained from environmentally and animal friendly farming. Therefore it could be suggested that the only way forward in animal production is further increases in efficency while reducing environmental pollution. This may be possibly achieved by a greater understanding of animal physiology and nutrition. A great number of studies are concerned with the effects of different foodstuffs, feed additives, as well as the size and frequency of feeding directly on farm animal performance. However, the interactions between particular dietary compounds and the physiological functions of farm animals are also elucidated. The latest includes the understanding of how the target tissue or the body system(s) of a production animal respond(s) to the individual nutrients; feed additives; chemical composition. An understanding of the amount or frequency Effects of Selected Feed Compounds and Feed Additives… 109 of feeding of a particular diet at a given stage of an animal‘s development is crucial in maintain animal health. From the physiological perspective, an animal‘s health or in a broader sense an animal‘s well-being is a principal foundation of an animal‘s growth, its reproductive capabilities, as well as high production performance. It is well acknowledged that any environmental factor that negatively affects animals‘ health inevitably decreases its productivity. This is especially true in case of animals during the period of their growth and development. The majority of young birds and mammals are vulnerable to environmental conditions. However, in case of farm animals the requirements‘ of breeding/farming management usually creates additional environmental pressures on their development. The weaning process in piglets is a good case in point. Under semi-natural conditions piglets are introduced to solid food, mainly plant based diet over a period of weeks and they are 16 to 20 weeks old before they cease suckling and ingest solid food only (Jensen and Recén, 1989). This slow process of weaning is concomitant with developmental processes of the whole organism including maturation of all gastrointestinal (GI) tract and its accessory glands functions. Under modern farming conditions piglets are weaned as early as two weeks of age and the weaning is usually abrupt so as to facilitate the efficacy of breeding and to reduce the cost of farm management. Whilst being cost effective in general, it frequently results in ill health of piglets manifested by e.g. reduced growth and or post-weaning diarrhoea. In such early-weaned animals, the functions of the GI tract and its accessory glands are still insufficient for digestion of a diet based on plant components. This is in addition to the behavioural stress related to the weaning that negatively affects the function of other body systems including immune system, which often results in increased mortality. Other health complications result from modern husbandry techniques e.g. housing of animals in large groups, often in crowded conditions, frequent movement and regrouping, or transportation. Although, some of these problems can be alleviated with improved farm management, animal nutrition per se plays a key role in disease prevention, especially in young animals, in which e.g. certain feed additives have been found to reduce scouring and mortality and concomitantly improved growth performance. It appears that GI motor or enzymatic functions respond well to diets/dietary supplements, which complement physiological insufficiencies of the developing GI tract. Thus, dietary management that complements /stimulates the animal‘s gastrointestinal tract for optimal development will also improve animal performance and thus efficacy of animal husbandry. The present work gives several examples of how GI physiology research helps to increase the understanding of problems related to modern animal nutrition.
Antibiotic Growth Promoters (AGPs)
Antibiotics were introduced to livestock and poultry production approximately five decades ago as a preventative measure of subclinical and clinical diseases, due to their ability to suppress or inhibit the growth of opportunistic microorganisms and pathogens. However, soon after their original incorporation into animals‘ production it was noted that the antibiotics in sub-therapeutic doses improved animal performance. For example, in growing- finishing pigs the improved performance included higher daily gain, better feed conversion ratio, as well as shortening of the time to reach a target weight (for review see Cromwell, 2002). Improved animal performance, together with the ease of application and the decreasing 110 Violetta Naughton and Patrick J. Naughton prises of feed antibiotics, contributed to a decrease in animal production costs and thus it is not surprising that the non-medicinal usage of antibiotics (antibiotic growth promoters, AGPs) in western countries grew steadily until the end of twentieth century. However, since the 1950‘s a growing body of scientific evidence has indicated a direct link between the development of antibiotic resistance in human pathogens and antimicrobial usage in animal production (Starr and Reynolds, 1951; Wegener et al. 1998; for review see Roe and Pillai, 2003). In recent years, the AGPs have been banned or their usage has been significantly restricted in many western countries, including European Union (from 1999). In Sweden and Denmark the AGPs usage in pig production has been stopped voluntary (1986 and 2000, respectively) and although retrospective removal of AGPs from the diets of growing-finishing pigs decreased the performance rather insignificantly (e.g. on average 2.5% in feed efficiency ratio), the effect on the young animals (nursery pigs) has been much greater (e.g. mortality rates increased by approximately 0.8%; Callesen, 2003). Although, a number of feed additives have since been developed and have tried to surplant the AGPs in piglets, they have not shown similar efficacy in this group of animals. The main effect of AGPs in animals, especially during their growth e.g. in nursery piglets is to prevent colonisation of the GI tract by the pathogens e.g. swine dysentery or pathogenistic Escherichia coli (Dibner and Richards, 2005) allowing application of husbandry techniques e.g. early weaning on a plant based diet, frequent movement and re- grouping. Although, the research on the effect of AGPs on gastrointestinal physiology has mainly concentrated on their modifying effects on the composition of the GI microflora, scientific literature indicates that the antibiotics per se show a wide spectrum of actions on GI tract functions. Antibiotics containing a lactone ring, macrolides have been shown to stimulate stomach and duodenal motility, accelerate gastric emptying, and inhibit motor activity of the distal part of the GI tract (Peeters et al. 1989). Thus, in medicine they substantiate the potential to regulate GI motility (Fraser et al. 1992; Tack et al. 1992). Mathis and Malbert (1995) have shown in pigs that oral erythromycin increases gastric motor activity, while in higher doses it can overcome the inhibition in intestinal motility induced by small intestinal nutrients, i.e. lipids. Erythromycin and other antimicrobials from the macrolide family are potent motilin agonists. Motilin is a GI peptide hormone that stimulates pepsin secretion, however its primary physiological function is to stimulate intestinal phase III contraction of the migrating motor complex (MMC) in the gut. The MMC is a physiological pattern of gut contractions occurring in the part of the stomach and small intestine during interdigestive state in animals including pigs, cattle, ship, goats, horses, dogs, cats, poultry and fish as well as in humans. The MMC, but especially phase III contractions, which occlude the intestinal lumen and migrate aborally, play an important role in sweeping clean the lumen of desquamated cells, residual ingesta and bacterial cells and thus promoting intestinal clearance and maintaining intestinal homeostasis. The regular occurrence of the MMC pattern is considered to be a physiological mechanism that prevents intestinal bacterial overgrowth (Vantrappen et al. 1977; Scot and Cahall, 1982). More importantly, it has been also demonstrated that the degree of bacterial colonisation in the gut does not only relate to the presence or absence of the MMC but to the MMC intensity (Soudah et al. 1991). Thus factors that disturb the regular occurrence of the MMC may lead to the disturbances of intestinal homeostasis. However, under physiological conditions i.e. food consumption, the MMC is Effects of Selected Feed Compounds and Feed Additives… 111 replaced by a so-called post-prandial motility pattern. Post-prandial motility pattern is characterised by the occurrence of contractions that do not occlude the lumen and move both orally and aborally thus promoting mixing of ingesta with digestive juices as well as exposing nutrients for absorption. Consequently, post-prandial contractions add to the efficiency of digestion and absorption of nutrients. However, in early-weaned animals such as piglets the secretion of digestive juices including gastric, pancreatic and intestinal does not match in quality and in quantity of the secreted enzymes the digestive requirements of a weaned diet as well as a new feeding regime (Cranwell, 1985; (Pierzynowski et al, 1990; Kidder I Manners, 1980). Neonatal piglets do not display post-prandial motility pattern in the gut when nursed by their dams (Lesniewska et al 2000a) or when fed commercial milk formula under a natural feeding regime i.e. every 1- 1.5 h with small quantities of liquid feed (Naughton et al. 2008). However, the MMC pattern is replaced with post-prandial pattern in piglets fed milk three times a day (Burrows et al. 1986; Szabo and Fewell, 1990). This relates to the increase in feed volume or its dry matter load, as has been shown previously in 3 – 4 weeks old piglets (Naughton et al. 2008). Also, it has been previously shown that in piglets directly after weaning solid feed consumed three times a day induced a long term post-prandial pattern both in the stomach (Lesniewska et al. 2000b) as well small intestine (Lesniewska et al 2000a). The implication of the long lasting post-prandial pattern in the small intestine concomitant with inadequate quantity and quality of digestive enzymes entail poor digestion of nutrients, especially proteins and resulting diminished absorption, and consequently increase availability of nutrients to the microbes, including pathogens in the upper parts of the GI tract. Furthermore, it may also lead to increased fermentation, higher production of short-chain fatty acids, and an overall change in the microbial population in the large intestine depending on the type of post-weaning diet as well as on the feeding regime. To our knowledge there are no data available on the effect of AGPs on the GI motility in piglets around weaning. However, it could be suggested that their possible effect on GI tract motility i.e. stimulation of re-occurrence of the MMC with its lumen occluding contractions, adds to the luminal clearance and thus helps maintaining gut homeostasis and prevents the gut environment shifting towards conditions suitable for potential pathogens and/or pathogens. Yet, antibiotics express other effects on GI tract functions. In human and veterinary medicine cephalosporins are commonly used against infections of the skin, urinary and respiratory tracts. However, in vitro studies suggest that cephalosporins release the gut hormone cholecystokinin from the upper gut endocrine system (Bozkurt et al. 2000). Cholecystokinin is known to stimulate pancreatic juice and bile secretion, and induce satiety. Thus, any broad spectrum antibiotics resembling penicillin could possible increase pancreatic juice and bile secretion and thus directly add to the efficient nutrients digestion in developing GI tract. Possibly, by improving satiety it could reduce the feed intake. This together with improved digestibility may improve overall feed conversion ration. In numerous reports on pigs as well as on poultry, antibiotics growth promoters have been demonstrated to decrease the weight of the small intestine due to thinning of the intestinal wall and shortening of the gut (Jukes et al. 1956; Stutz et al. 1983). It has been shown that dietary supplementation with avoparcin can reduce cell proliferation in the small intestine of broiler chickens (Krinke and Jamroz, 1996). Also, the stimulatory effects of antibiotics in subtherapeutic doses on the immune system, e.g. increased serum IGF-I concentration (Hathaway et al. 1996) have been reported. 112 Violetta Naughton and Patrick J. Naughton
To summarise, the results of scientific studies from across different disciplines indicate that antibiotics used in subtherapeutic doses not only inhibit or destroy pathogens but are also able to alter the functions of the GI tract and its accessory glands as well as influencing the immune function of the organism. It is therefore not surprising that animal production such as pig husbandry without AGPs is extremely challenging.
Feed Acidifiers
It is proposed that acidification of the diet may provide a prophylactic measure similar to that provided by feed antibiotics. While antibiotics are designed to inhibit microbial growth in general (Cromwell 1990), the acidifier would cause the beneficial rather than harmful microorganisms to dominate in the gastrointestinal tract (Mathew et al. 1991). Supplementation of the diet for weanling pigs with acidifying agents has been shown, in many cases, to increase average daily weight gain, gain to feed ratio and reduce the incidence of diarrhoea (Gabert and Sauer, 1994, Partanen, 2001). A number of studies have indicated that organic acids may improve the performance in fattening pigs too (Mosenthin et al. 1992; Kemme et al., 1995; Mroz et al., 1997, Partanen, 2001) as well as reducing nitrogen and phosphorus excretion (Jongbloed and Jongbloed, 1996). However, the performance response to dietary supplementation with feed acidifiers is often variable and recorded response to the acidifiers varied from significant to none. This has led to the investigation of the possible modes of action of the acidifying agents. Although several hypotheses have been proposed, the exact mechanisms still remain unclear. It is generally considered that feed acidifiers lower gastric pH, resulting in increased gastric proteolytic enzyme activity, and improved regulation of gastric emptying. Furthermore, acids entering the duodenum stimulate the exocrine pancreas to secrete bicarbonates. Therefore, it was expected that feed acidifiers would increase exocrine pancreatic secretion. The hypothesis that lowering dietary pH with feed acidifiers reduces gastrointestinal pH has been tested in several studies. However, only a few of them have shown that dietary acidification significantly decreased gastric pH (Bolduan et al. 1988a, 1988b; Roth et al. 1992, Jensen, 1998). Moreover, the results of the studies cited above indicate that diet acidification has no effect on dry matter content in the stomach. Hence, the dry matter content in the stomach is proportional to the rate of gastric emptying, the regulatory effect of feed acidification on gastric emptying is not clear. Reduced gastric emptying would be desirable in weaning piglets, as it would allow more time for gastric protein digestion. It would also decrease high outflow of ingesta to the small intestine thus reducing the risk of a long lasting post-prandial pattern. The results of a study by Gacsalyi et al. (2000) indicated that dietary supplementation with an encapsulated blend of acidifiers (Aciprol) had influence intestinal digesta flow. In this electromyography study, the diet of growing pigs was supplemented with Aciprol leading to an increase in the MMC per day and shortening of the postprandial pattern when compared to pigs fed the diet without supplementation. So far, little is known about the influence of dietary acidification on exocrine pancreatic secretion and available results are contradictory. Thaela et al. (1999) observed that supplementation of the feed for weaned piglets with lactic acid increased exocrine pancreatic secretion in volume, total protein, and trypsin activity when compared with the diet without Effects of Selected Feed Compounds and Feed Additives… 113 supplementation. However, the results of our study on weaning piglets showed that supplementation of the diet with lactic or formic acid had no effect on basal and acid- stimulated exocrine pancreatic secretion (Lesniewska et al. 2000c). As in the case of the effect of feed acidifiers on exocrine pancreatic secretion, there is very little data available on the influence of dietary acidification on gut morphology. Galfi and Bokori (1990) observed that Na-butyrate in the diet resulted in a substantial increase in the number of cells constituting the microvilli, and in the length of microvilli in the ileum of growing pigs. Whether other dietary acidifiers have similar effects is not known. Although, the result of most studies indicate that dietary acidifiers have beneficial effects on the pigs‘ performance, both during post-weaning and fattening periods, the physiological mode of actions of these supplements is still not clear. Without this knowledge their commercial use may not be financially efficient in every day use in modern farm production.
Dietary Proteins, Peptides, and Amino Acids
Dietary proteins are one of primary dietary components, and their quality (here amino- acids composition; ration etc.) and quantity have direct influence on animal health, growth and performance. In both cattle and pigs, proteins are digested and absorbed in the upper part of the GI tract, although via different digestive mechanisms. Consequently, the proteins, peptides or amino acids that are not digested, absorbed in the stomach and small intestine respectively are used by microbes in the large intestine, and although the end products of the bacterial fermentation are useful to the host, the high amount of protein/peptides/ amino acids in the large intestine increases putrefaction, which is detrimental both to the host as well as the environment. In young piglets, as it was mentioned previously, the digestive capacities of the GI tract and its accessory glands are insufficient to efficiently digest any other diet then maternal milk. This insufficiency includes secretion of gastric acid, as well as gastric, pancreatic and intestinal enzymes responsible for the protein digestion. In adult animals all physiological functions respond to the change of the quality and quantity of diet. However, while in a pig from post-weaning period onwards the GI tract can adapt to the new diet within 48- 72 hrs, in young animals it takes much longer (e.g. up to 7-10 days after weaning; Rantzer et al., 1997), as it entitles developmental changes (i.e. maturation) and not just an adaptation. The results of number of studies (Bikker at al., 2006; Nyachoti et al., 2006; Htoo et al., 2007; Opapeju et al., 2009) indicate that the low-protein diet supplemented with essential amino acids may be beneficial for pigs during post-weaning period. The results of these studies have shown that the decrease of the crude protein content in piglets‘ diets resulted, amongst other in a lower concentration of ammonia in the small and large intestine, which would indicate reduced protein fermentation. Notably, the growth performance of the piglets in all three studies has not been affected, while diarrhea was not observed. However, a number of earlier studies on level of total protein in diets for weaned piglets have show lack of the effect of low-protein diet on the incidence of diarrhoea (Armstrong and Cline, 1977; Pouteaux at al., 1982). It could be speculated that although the overall decrease of crude protein in the post- weaning diet can prevent overall wastage of protein the gut, it may be not enough to assure 114 Violetta Naughton and Patrick J. Naughton the maintenance of the intestinal homeostasis during post weaning period in case of poor general hygiene and / or lacking husbandry.
Proteins and Peptides as Feed Additives
The protein source may have different effects on the gastrointestinal tract due to its components and thus its influence on gastric emptying and small intestine motility. In calves, the milk replacers based on cows milk or cows milk protein (casein) remain in the abomasum longer due to chymosine clotting. Fish protein on the other hand, which is devoid of this activity, produces different patterns of gastric emptying and intestinal flow of digesta (Guilloteau et al. 1975, Guilloteau et al. 1981). Consequently, as it has been shown in calves, non-milk proteins disrupts the normal motility profile in the small intestine: the post-prandial pattern is longer and the first few postprandial MMCs are of irregular duration (Zabielski et al. 1998). In addition, the source of dietary protein has an impact on gastrointestinal endocrine function. In the same study, the profile of gut regulatory peptides was also affected. The periodic oscillations of gut regulatory peptides (PP, somatostatin) lost their regularity or completely disappeared (secretin, motilin). As already mention the presence of periodic activity (specific for age and animal species) in the gastrointestinal tract is a sensitive and reliable marker of animal health (Zabielski, Naruse, 1999). Over last few decades, plasma proteins (spray-dried plasma) is often used as an additive to weaned pigs diet as it has been shown to enhance the pig performance (Ermer at al., 1994; Coffey and Cromwell, 2001; van Dijk et al., 2002, Zhao et al., 2007). Furthermore, number of studies on weaned pigs challenged with different pathogens showed that addition of dried plasma protein (both of bovine as well as porcine origin) resulted in reduction of mortality (Bikker, et al., 2004; Torrallardona et al., 2007). Spray-dried plasma has been shown to have direct effect on the intestinal mucosa thus reducing intestinal inflammation, preventing mucosal permeability, as well ass improved nutrient absorption (Peres-Bosque et al., 2004; Garriga et al., 2005; for review see also Moreto and Perez-Bosque, 2008). However, it has been shown earlier that higher level of plasma powder may cause non-infectious diarrhoea in piglets (Nollett et al., 1999), which could be attributed to the higher level of proteins reaching the large intestine. Specific protein molecules or bioactive peptides – products of food protein hydrolysis in the gastrointestinal tract – may also have individual effects on gastrointestinal tract function. Bioactive peptides derived from milk proteins are resistant to biodegradation in the GI tract, and their concentration in the digesta is remarkable (e.g., from 1 g kappa-casein approximately 60 mg casoxin A can be obtained) (Meisel 1998). Animal- and plant-derived bioactive peptides have been studied less intensively than milk-derived ones, although they are present in a variety of protein sources (Table 1). These type of proteins and peptides are attracting attention in human nutrition research on food substances able to modulate the physiological responses of the human body. The function of bioactive peptides in the GI tract of intensively growing production animals is of great interest as well. Bioactive peptides derived from animal and plant proteins can potentially be utilised to regulate the development of gastrointestinal tract function, optimise its motility, modulate enzyme activity and nutrient absorption. However, it has to be stressed out that not all of them show beneficial effects, e.g., coeliac-toxic peptides (Table 1). Effects of Selected Feed Compounds and Feed Additives… 115
Table 1. Examples of bioactive peptides derived from animal and plant protein hydrolysates important for gastrointestinal tract function (Dziuba et al. 1999, Wong et al. 1996, Tirelli et al. 1997)
Peptide family Bioactive peptide Protein precursor Hydrolysis Exorphins: Wheat gluten GYYPT, YGGW, YPISL Opioid agonists and Haemorphins: Pepsin antagonists VVYPWTQRF, Bovine haemoglobin LVVYPWTQRF Oryzatensin and Smooth muscle oryzatensin-C-terminal Rye albumin contracting and fragment Trypsin Immunomodulating HCQPR Soybean peptides Sulfated glucopeptides Egg proteins LLPHH, VIPAGYP Soy Proteinase S Antioxidative peptides ? Bovine elastin Elastase Wheat gliadin fragments: Wheat, barley, oat and Pepsin, trypsin, Coeliac-toxic peptides QQPYPQ, YPQPQ rye gluten pancreatin
Table 1 presents a small selection of known BAP of animal and plant origin that possess experimentally documented effects on the GI tract. Opioid agonists and antagonists are known to be ligands of opioid s-, m- and k- receptors, and thus reveal similar biological effects as the endogenous opioids. In contrast with opioids, the mechanism of action of most BAP needs further research.
Amino Acids and Their Derivatives as Feed Additives
It is well known that a deficiency of essential sulphur-containing amino acids, methionine (Met) and/or cysteine, often limits animal production (Campbell et al. 1997). Therefore, they are frequently used as feed additives. Methionine, however, readily oxidises to sulfoxide derivatives (Gjoen and Nijaa, 1977). For example, Met in protein can be oxidized by the hydrogen peroxide that is used to sterilise milk and whey (Rox and Kosikowski, 1967) and to detoxify protein concentrates (Anderson et al. 1975). Heat, gamma-irradiation, extrusion, and long-term storage are also known to catalyse the oxidation of protein (Puchala et al. 1994). Methionine sulfoxide (MSO), a Met oxidation product, is formed readily under mild oxidative conditions (Yamasaki et al. 1982). Electromyography studies suggest that Met as well as MSO affect intestinal motility. Puchala et al. (1997) in a study on pre-ruminant calves have shown that intraduodenal infusion of Met, when applied in a higher dose, increased the frequency of the MMC. Increased migration of MMC indicates faster digesta flow, which will decrease the time required for absorption of nutrients. On the other hand, it could be speculated that such an effect in piglets during post-weaning period may be beneficial as it will prevent colonisation of the upper GI tract by the pathogens. In the same study, Puchala et al. (1998) have shown that the effect of intraduodenal infusion of MSO on intestinal motility 116 Violetta Naughton and Patrick J. Naughton was much different than that of Met. MSO increased the duration of phase II of MMC and did not change the migration of MMC. Hence the absorption of nutrients from the intestine occurs during phase II of MMC (Bueno and Fioramonti, 1994), elongation of phase II with no changes in velocity of MMC suggests a longer presence of nutrients for absorption, which could be beneficial in adult animals. Indeed, infusion of MSO increased the plasma concentration of arginine, valine and phenylalanine, which suggests increased absorption from the small intestine. In weaning piglets, however such effect e.g. decrease frequency of MMC could be rather detrimental due to the increase bacterial colonisation of the gut. Yet other compounds, e.g. betaine and choline, which are donors of a free methyl group, are used for supplementation of animal diets. Betaine and choline are not considered to be essential nutrients as such, but they are important in many metabolic pathways (Mitchell et al. 1979; Burnham et al. 1996; Shronts, 1997). It has been shown that oral betaine supplementation increased the Met concentration in plasma taken from human subjects (Storch et al. 1991). Dietary choline supplementation in dairy cows increased the percentage of milk fat (Erdman et al. 1984). Gralak et al. (1998) have shown that supplementation of the diet with choline has a positive effect on the production performance of half-year-old calves. However, Puchala et al. (1998) in an electromyography study on the effect of intraduodenal infusion of choline and betaine in calves, observed that both compounds in high doses might decrease nutrient absorption in the small intestine by increasing digesta transport. Recently, it has be suggested that both deficiency of excess of yet another amino acid i.e., Threonine affects the intestinal mucosal barrier in piglets (Wang et al., 2010), and thus the level of Threonine should be taken into consideration when designing diets for nursery piglets. To summarise, dietary proteins display numerous, direct and indirect effect of the GI tract physiology and could be successfully used as feed additives for animals as heath promoting agents. However, the outcome of utilisation of nutritional proteins, bioactive peptides, amino acids on the animal performance depends on many factors, which should be take into account when formulating diets for particular animal species at given stage of their development.
Dietary Lectins
Lectins are well known antinutritional factors that are present in many plants used in animal nutrition. Feeding with feed containing raw lectins leads to the development of gastrointestinal disturbances and reduces animal performance. On the other hand, lectins isolated from the red kidney bean (Phaseolus vulgaris) have been shown to promote the growth of the GI tract and influence metabolism by affecting hormone balance, e.g. insulin, in the body (Pusztai et al. 1999, Radberg et al. 2001). The red kidney bean lectin changed the spatial as well as the temporal maturation by speeding up the maturation of enterocytes, thus changing the protein expression and function through increased levels of alkaline phosphatase and brush border disaccharidases in rats (Pusztai, 1999), and reducing the absorption of marker molecules in vivo and in vitro in suckling piglets (Radberg et al. 2000). The lectin-treated pigs had increased crypt depth and reduced villi length in the jejunum (Table 2). Effects of Selected Feed Compounds and Feed Additives… 117
Table 2. Morphometry measurements of the small intestinal mucosa (mm), the contribution of vacuolated enterocytes on the intestinal villi, and the area of enterocyte large vacuoles (LV) in the middle jejunum of control pigs and lectin-treated pigs. Adapted from Radberg et al. (2000)
Tunica Enterocytes Area of LV Crypt depth Villi length mucosa with LV (%)a (µm2) Control 155 ± 7 773 ± 69 986 ± 74 97 103 ± 2 Lectin - 157 ± 8 627 ± 31** 815 ± 45 25 49 ± 2*** treated The results are presented as mean ± SEM (n=8) and compared statistically where *P<0.05, **P<0.01 and ***P<0.001 were significant. aLarge vacuoles (LV) take a part in nonselective trans-epithelial transport of molecules in newborns. The percentage of enterocytes containing large vacuoles is an index of intestinal mucosa maturity - fewer vacuolated enterocytes are observed in mature intestinal mucosa.
These changes in gut morphology and molecule absorption indicate that the red kidney bean lectin speeded up enterocyte replacement (turnover) so the gut mucosa appears to be more mature in a way normally associated with pigs after weaning. This study showed a way of manipulating gut development and maturation that could be of potential use in animal production. Plant lectins have been subject to a number of articles in the past decade (Pusztai et al 2008, Vasconcelos and Oliveria 2004, Vandamme 2008),. However, the majority of these reviews have focussed on the biomedical potential of lectins in terms of new treatments (Puszati et al 2008) and much less on the potential of lectin containing animal feeds (Enneking and Wink 2000, Grant 1999). Obviously much of the focus has been on the fact that certain feedstuffs are known to be toxic to animals and this knowledge dates back to Stillmark (1888). But with attention now focussing on food security and sustainability, attention has refocused on the potential of lectin containing crops as animal feed. Genetic engineering now has the potential to reduce the expression of lectins in under-utilised crops to an extent where they become a valuable feedstuff while retaining the innate protective role of these carbohydrate binding lectins against microorganisms (Naughton 2001) and insects (Powell 2001, Ripoll et al 2003)). Owing to the history of lectins and their ‗toxic‘ nature, the vast majority of the work, looking at the effects of lectins in the diet, has been carried out in experimental animals, namely rats and mice. Much of this has been to do with the necessity to use smaller animals because of the cost of purifying the lectins of interest and much less work has been done in feed animal‘s e.g cattle, sheep and pigs (Le Gall et al 2010). In experimental animals the typical symptoms of poor appetite, loss of weight and death have emerged from studies on legumes (Lajolo and Genovese 2002). PHA has been shown to increase maturation of the GI tract in suckling rats (Linderoth et al 2005) and Soyabean agglutinin (SBA) has been shown to have deleterious effects in chicks (Douglas et al 1999) but to a much lesser extent that that found in rats (Liener 1953). In order to get a true picture of the effects of lectin containing feedstuffs it is necessary to carry out the studies in the species of interest e.g. Pigs have shown an increase in stomach weights and mucosal thickness as a result of PHA treatment (Radberg et al 2001) and it has 118 Violetta Naughton and Patrick J. Naughton also been shown to illicit a strong IgG like response when raw beans were fed to growing steers (Williams et al 1984) and pigs (Begbie and King 1985). Grain legumes are probably the most promising plant proteins sources in terms of the feed industry (Mikic et al 2009). Grain legumes contain a number of anti-nutrient factors and lectins play a role to a greater or lesser extent depending on the legume involved e.g. KTI has been shown to have a greater anti- nutritional effect on chicken growth in comparison to SBA (Douglas et al 1999). While soyabean genotypes lacking SBA have been found (Pull et al 1978), the future lies in the development of genetically modified crops. Since lectins are gene encoded proteins they are excellent targets for modification. This approach may allow the down-regulation of lectin synthesis leading to lectin free crops and since lectins mediate insect toxicity, the transfer and expression of lectin genes may create a means of enhancing their resistance to insects and reduce our reliance on synthetic insecticides.
Bacteria and Lectins
The interaction between plant lectins and bacteria have been well described (Sharon 2008, Naughton 2001) and the potential of lectins in ameroliating the effects of gut pathogens and gut infections have been explored in a number of publications (Pusztai 1991, Naughton 2001, Naughton et al 2000). The lectin GNA from Galantus Nivalis has been shown to reduce numbers of Salmonella in the rat gut in vivo (Naughton et al 2001). Despite the vast amount of data available on the interactions between specific plant lectins and pathogens (Sharon 2008), the potential of lectins in the fight against infection has been stymied somewhat by the expense of purifying plant lectins and the difficulties in expressing lectins or lectin analogues in modified bacteria (Ling et al 2009) and yeast (Baumgartner et al 2003). The future potential of lectins in this area is dependant on the production of cost effective analogues and/or the expression of lectins in plants from which they can be easily purified.
Reduction of Lectin Expression in Feed Crops
In the last decade most attention has been directed at the potential of plant lectins in transgenic plants to improve resistance to pests and insects (Trung et al 2006) and a number of lectins including GNA (from the Snowdrop) have been utilised in this way. However, much less attention has been given to the potential of genetic engineering to reduce or completely eliminate the expression of lectins in what would be otherwise extremely efficient feed crops. To date, conventional processing systems have been used to reduce the ‗anti- nutrient‘ elements of feed crops (Enneking and Wink 2000). In the case of lectins, which are heat labile, these can be denatured during the grinding and pelleting of legume seeds for the food industry (Enneking and Wink, 2000). The and use of proteases for the treatment of vegetable proteins has been subject to a number of patents (Ostergaard et al 2009).The advent of ‗antisense‘ technologies has opened the possibility of suppressing or completely eliminating lectin expression in many crops (Frizzi and Huang 2010). There are a number of examples of how this antisense technology has already been used in terms of reducing toxicity e.g Solandine in potatoes (McCue et al 2003). As yet, there is very little evidence in the literature of work to remove lectins from feed crops but with the rise in the world Effects of Selected Feed Compounds and Feed Additives… 119 population and the emergence of the economies in Asia there will be ever increasing pressure on food production and feed crops and there may yet be a refocus of attention on legumes. However, a balance will need to be found between suppressing lectin expression while retaining the insecticidal benefits of these lectins for plants (Newell-McGloughlin 2008)
CONCLUSION
The digestive system is a very complex organ with number of physiological functions, primarily digestion and absorption of nutrients, but also it provides defence against environmental traits as well as it is largest endocrine organ in the body. The proper overall functioning of digestive system, with all its accessory glands results from a number of both endo- and exogenous factors. Nutrients, the exogenous factors have direct or indirect effect on the functioning of digestive system, and consequently on the organism as a whole. In order to progress to more efficient, environmentally and animal friendly change in animal production more knowledge is needed on alternative ways for improving animal health. The physiological function of the digestive system is of major concern in this context.
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Chapter 6
APPLICATION OF WAVELET NEURAL NETWORKS AS A NON-LINEAR MODELLING TECHNIQUE IN FOOD MICROBIOLOGY
V. S. Kodogiannis1, M. Amina1, J. N. Lygouras2 and G. J. E. Nychas3 1Computational Intelligence Group, School of Electronics and Computer Science, University of Westminster, 115 New Cavendish Street, London W1W 6UW, United Kingdom, 2Dept. of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, GR-67100, Greece 3Laboratory of Microbiology and Biotechnology of Food, Dept. of Food Science and Technology, Agricultural University of Athens, Athens, GR-11855, Greece
ABSTRACT
The need for intelligent methods to model highly nonlinear systems is long established. Feed-forward neural networks have been successfully used for modelling of non-linear systems. The main features of these systems such as the ability to learn from examples and to self-adapt are very well suited for the multi-resolution approach intrinsic to wavelets. Wavelets offer an adequate framework for the representation of ―natural‖ signals that are described by piece-wise smooth functions, with rather sharp transitions between neighbouring domains. The combination of wavelet theory and neural networks has lead to the development of wavelet networks (WNNs). The aim of this research study is to investigate the modelling capabilities of modified WNNs, where the connection weights between the hidden layer neurons and output neurons have been replaced by a local linear model, for describing the inactivation pattern of Listeria monocytogenes by
Correspondent Author: Dr. Vassilis Kodogiannis, Email: [email protected] 128 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
high hydrostatic pressure in milk, and to evaluate its performance against classic neural network architectures and models utilised in food microbiology. Milk was artificially inoculated with an initial population of the pathogen of ca. 107 CFU/ml and exposed to a range of high pressures (350, 450, 550, 600 MPa) for up to 40 min at ambient temperature (ca. 25°C). Models were validated at 400 and 500 MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas all learning-based networks were utilised in a standard identification approach. The prediction performances of the proposed learning- based networks were better at both validation pressures. The development of accurate models to describe the survival curves of microorganisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process.
1. INTRODUCTION
There has been continued interest in the food industry in using high hydrostatic pressure processing as a non-thermal preservation technique. Its primary advantage is that it can inactivate microorganisms and certain enzymes at ambient temperature, thus avoiding the detrimental effects of cooking temperatures on various food quality attributes, such as nutritional value, flavour and taste. A variety of pressure-treated commercial products such as jams, fruit juices, guacamole, and fresh whole oysters, are already commercially available in the United States, Japan and Europe, whereas another potential application in the food industry is the production of novel meat, poultry, fish and dairy products. However, as foods are frequently implicated as carriers of foodborne pathogens, it is important to provide information on the effect of high-pressure processing on these micro-organisms. The inactivation of micro-organisms by high pressure is well documented; typically, vegetative pathogens can be inactivated at a pressure range of 200–700 MPa [1]. The exact mechanism of high pressure inactivation has been fully elucidated, but it is generally accepted that high pressure results in morphological, genetic and biochemical alterations causing cell death due to accumulated damage [2]. Listeria monocytogenes is a ubiquitous foodborne pathogen associated with outbreaks of listeriosis from consumption of various food commodities, such as vegetables, dairy products, seafood and meat [3]. The pathogen is of great health concern for the food industry, because it is characterised by high mortality rates, especially in pregnant women, neonates, elderly and immune-compromised. The pathogen can grow at refrigeration temperatures and survive in foods for prolonged periods of time under adverse conditions. It is a very hardy micro- organism that can grow over a wide range of pH values (4.3 to 9.1) and temperature ranges from 0 to 45°C. In addition, it is relatively resistant to desiccation and can grow at aw values as low as 0.90. To establish a process that is sufficiently good for the safety of a food commodity, the pressure-destruction kinetics of spoilage and pathogenic micro-organisms related to the specific product should be established and described in detail [4]. The inactivation of micro-organisms by heat and other processing methods has been traditionally assumed to follow first-order kinetics. All cells or spores in a population are assumed to have equal resistance to lethal treatments, and therefore a linear relationship between the fall in the logarithm of the number of survivors over treatment time would be expected. However, significant deviations from linearity have frequently been reported [5]. Three kinds of Application of Wavelet Neural Networks… 129 deviations have been observed: curves with a shoulder, curves with tailing, and sigmoid-type curves. A number of models have been proposed to describe these nonlinear survival curves, such as the Weibull [6], modified Gompertz [7], Baranyi [8], Chiruta [9], and the Xiong [10] models. Developing models from observed data is a fundamental problem in many fields, such as statistical data analysis, signal processing, control, forecasting, and computational intelligence. This problem is also frequently referred to as function estimation or approximation and system identification. There are two general approaches to function learning, namely, the parametric and the non-parametric approach. When the observed data is contaminated (such that it does not follow a pre-selected parametric family of functions or distributions closely) or when there are no suitable parametric families, the non-parametric approach provides more robust results and hence, is more appropriate. Neural networks (NNs) have become a popular tool in non-parametric function learning due to their ability to learn rather complicated functions. The multi-layer perceptron (MLP), along with the back-propagation (BP) training algorithm, is probably the most frequently used type of neural network in practical applications [11]. However, due to its multilayered structure and the greedy nature of the BP algorithm, the training processes often settle in undesirable local minima of the error surface or converge too slowly. The radial basis function (RBF) network, as an alternative to the MLP, has a simpler structure. With some pre- processing on the training data, such as clustering, the training of RBF networks can be much easier than MPL networks. From the point of view of function representation, an RBF network is a scheme that represents a function of interest by using members of a family of compactly (or locally) supported basis functions. The locality of the basis functions makes the RBF network more suitable in learning functions with local variations and discontinuities. Furthermore, the RBF networks can represent any function that is in the space spanned by the family of basis functions. However, the basis functions in the family are generally not orthogonal and are redundant. This means that for a given function, its RBF network representation is not unique and is probably not the most efficient. In literature, some unsupervised clustering techniques were widely used for RBF centre determination according to the input disturbances [12]. In recent years, wavelets have become a very active subject in many scientific and engineering research areas. Especially, wavelet neural networks (WNN), inspired by both the feed-forward neural networks and wavelet decompositions, have received considerable attention and have become a popular tool for function approximation [13]. The main characteristic of WNNs is that some kinds of wavelet functions are used as the nonlinear transformation function in the hidden layer, instead of the usual sigmoid function. Incorporating the time-frequency localisation properties of wavelets and the learning abilities of general neural network, WNN has shown its advantages over the regular methods such as NN for complex nonlinear system modelling [14]. The aim of the current research study is to investigate the feasibility of utilising WNN methodology as an alternative to classical neural networks in the area of food microbiology. In this research study, two WNN schemes that incorporate some modifications compared to classic WNNs, in order to enhance their performances are proposed. A classic WNN employs nonlinear wavelet basis functions (named wavelets) instead of using common sigmoid activation functions. The output of the network is a weighted sum of a number of wavelet functions. In the first proposed linear-weights wavelet neural network (WNN-LCW), the 130 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al. connection weights between the hidden layer neurons and output neurons are replaced by a local linear model, similar to the output layer appeared in ANFIS neuro-fuzzy system. In a further modification, the multiplication wavelet neural network with local linear weight coefficients (MWNN-LCW) incorporates a ―product-operation‖ layer adopted from the basic neuro-fuzzy Larsen architecture [15]. The overall objective of this study is to design an accurate one-step-ahead prediction scheme to model the survival of Listeria monocytogenes in ultra high-temperature (UHT) whole milk during high pressure treatment using the proposed WNN structures. The proposed prediction schemes is compared against traditional neural networks and a linear PLS regression model. Similarly, an assessment will be made against two well-known for the food microbiology, nonlinear conventional models (Weibull, Gompertz) and an evaluation will be conducted to compare the goodness-of-fit of these models.
2. EXPERIMENTAL CASE
2.1. Bacterium and Preparation of Cell Suspension
Listeria monocytogenes NCTC 10527 from the collection of the Laboratory of Microbiology and Biotechnology of Foods was used throughout this study. Stock cultures were maintained in vials of treated beds in a cryoprotective fluid (Protect Bacterial Preservers, Technical Service Consultants Ltd., Heywood, UK) at -80°C until use. The culture was revived by inoculation in 9ml of Tryptic Soy Broth (TSB, 402155, Biolife, Milan, Italy) supplemented with 0.6% yeast extract and incubation at 30°C for 24h. For experiments, a loop of the culture was transferred into 9ml of the same medium and sub-cultured twice at 30°C for 24h. The cells were harvested by centrifugation (6,000 rpm for 30 min at 4°C), washed twice with sterile phosphate-buffered solution, re-centrifuged, and re-suspended in the same diluent to give a final concentration of about 9.0 log CFU ml-1, as assessed by a Neubauer counting chamber (Brand, Wertheim, Germany). This inoculum was used in all experiments.
2.2. High Pressure Equipment
The high-pressure system (Resato International B.V., Roden, Holland) consists of a high- pressure intensifier unit for the build-up of pressure in the system and an electric motor to drive a hydraulic pump. The oil in the pump is used to propel the oil-driven double-acting intensifier, which is actually a hydraulically driven reciprocating pump. In the intensifier, the pressure of the high-pressure fluid (Resato International B.V. high-pressure fluid-glycol emulsion) is ranged up to 1000 MPa. The pressure is adjustable in steps of approximately 25 MPa. Moreover, the system consists of a block of six small (42ml) high-pressure vessels measuring 2.5cm in diameter and 10cm in length, respectively. The vessels are closed with a unique Resato thread connection on the top of the vessel. The pressure is transmitted from the intensifier to the vessels by the pressure fluid through high-pressure stainless steel tubing. Air-operated high-pressure needle valves are used for the control of circulation of pressure Application of Wavelet Neural Networks… 131 fluid, so that each vessel operates independently. Each vessel is equipped with a heating/cooling jacket to control experimental temperature in a range from –40 to +100 °C. Temperature transmitters are mounted in each vessel to monitor temperature. Finally, two pressure transducers are used to monitor the pressure in the system.
2.3. Pressurisation of Samples
Aliquots of 3.0ml of sample containing 0.3ml of cell suspension and 2.7ml of UHT whole milk were transferred in polyethylene pouches (20mm width x 80mm length) and heat- sealed taking care to expel most of the air. Every pouch was placed in a second slightly bigger one to prevent accidental leakage of cell suspension and contamination of the pressurizing liquid. The pouches were placed in duplicate in each of the six small vessels of the high pressure unit and the system was pressurised at 350, 450, 550 and 600 MPa, respectively. The initial temperature of the vessel jacket was adjusted to 25°C by means of a water-glycerol solution circulating from a water bath. The come-up rate was approximately 100 MPa per 7s and the pressure release time was less than 3s. At selected time intervals, pressure levels were isolated individually from the pressure system and the pressure of this vessel was released. Pressurisation time reported in this work did not include the pressure come-up and release times. Each experiment was repeated twice and duplicate pouches were used for each pressure/time combination.
2.4. Enumeration of Survivors
Immediately after treatment, pressurized pouches were removed from the vessels and their contents were aseptically diluted in ¼ sterile Ringer‘s solution (Merck 1.15525, Darmstadt, Germany). One hundred microlitres (100μl) of at least three serial dilutions were spread-plated in triplicate on the non selective Tryptic Soy Agar (TSA) medium supplemented with 0.6% yeast extract for the enumeration of L. monocytogenes. The plates were allowed to incubate at 30ºC for 48h to form visible colonies, and then re-incubated at the same temperature for an additional 24h to allow injured cells to recover. The data from the plate counts were transformed to log10 values prior to further analysis.
3. MODEL DEVELOPMENT
3.1. Primary Modelling
The survival curves of L. monocytogenes during high pressure inactivation were fitted with two primary models to determine the kinetic parameters of L. monocytogenes in UHT whole milk. The first model applied was the re-parameterized Gompertz equation [16], determined by the following equation:
132 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
k e (1) log10 N(t) log10 N(0) A exp exp ts t1 A
-1 where ts [min] is the duration of the shoulder, k [min ] is the maximum specific inactivation rate, N(0) [log CFU ml-1] is the initial population density of the pathogen, and A [log CFU ml- 1] is the difference between the initial and residual population. The second model was based on the modified Weibull equation [17] which can be defined as:
p t log N(t) log N(0) N 10 (2) 10 10 res res where δ [min] is a scale parameter denoting the time for the first decimal reduction, and p [dimensionless unit] is the shape factor of the curve. For p > 1, convex curves are obtained -1 whereas for p < 1 concave curves are described. Finally, N(0) and Nres [log CFU ml ] are the initial and residual population of the pathogen, respectively. Models were fitted by the nonlinear regression procedure in Statistica software version 6.0 (Statsoft Inc., Tulsa, Okla.).
3.2. Non-Parametric Modelling
Partial least squares (PLS) regression, a multivariate calibration technique, projects the initial input-output data down into a latent space, extracting a number of principal factors (also known as latent variables) with an orthogonal structure, while capturing most of the variance in the original data. In brief, it can be expressed as a bilinear decomposition of both X and Y as:
T XETW X (3) and
T YEUQ Y (4) such that the scores in the X-matrix and the scores of the yet unexplained part of Y have maximum covariance. Here, T and W, U and Q are the vectors of X and Y PLS scores and loadings (weights), respectively, while EX, EY are the X and Y residuals [18]. The decomposition models of X and Y and the expression relating these models through regression constitute the linear PLS regression model. In case of one Y-variable, y , the model can be expressed as a regression equation
y bX E (5) Application of Wavelet Neural Networks… 133 where b is the regression coefficient. The PLS model is developed in two stages; the initial dataset is divided into training and testing subsets. The former dataset is used to build the models and compute a set of regression coefficients (bPLS), which are subsequently used to make a prediction of the dependent variable in the test subset. Multilayer Perceptron structure is probably the most widely used neural network paradigm and has long proven nonlinear modelling capabilities/performance. The knowledge of the network is stored in the weights connecting the artificial neurons. The massively interconnected structure of the MLP provides a great number of these weights and as such a great capacity for storing complex information. The generalised delta rule is applied for adjusting the weights of the feedforward networks in order to minimise a predetermined cost error function [11]. The rule of adjusting weights is given by the following equation:
p p p p p wij (t 1) wij (t) j y j wij (t) (6) where is the learning rate parameter, the momentum term, and is the negative derivative of the total square error with respect to the neuron‘s output.
4. WAVELET NETWORKS
4.1. Wavelet Transform
Wavelet techniques can offer added insight and performance in data analysis situations where Fourier techniques have previously been used [19]. The basis functions of the Fourier transform consist of sine and cosine, while basis functions of wavelet transform consist of the dilated and translated versions of the mother wavelet. The basis functions for both transforms are localised in frequency, but wavelet functions are also localised in space. The time- frequency resolution differences between the windowed Fourier transform (WFT) that is used to localise in space and the wavelet transform is the basis function coverage of the time- frequency plane based on the uncertainly property. The resolution of the WFT analysis is the same at all locations in the time-frequency plane, because a single window whose width varies is employed in the wavelet transform. The wavelet transform (WT) in its continuous form provides a flexible time-frequency window, which narrows when observing high frequency phenomena and widens when analysing low frequency behaviour. Thus, time resolution becomes arbitrarily good at high frequencies, while the frequency resolution becomes arbitrarily good at low frequencies. This kind of analysis is suitable for signals composed of high frequency components with short duration and low frequency components with long duration, which is often the case in practical situations. Here, a brief review from the theory of wavelets is described that provides the main ideas about wavelets and the related work. Wavelet transform is divided in two parts: continuous wavelets transform (CWT) and discrete wavelets transform (DWT). Historically the CWT was the first studied of the wavelet transform. Let ft()be any square integrate-able function. The signal or function can be expressed as:
134 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
tb f( t ) W ( a , b ) d b d a (7) a where, W(,) a b is continuous wavelet transform of ft()with with respect to a wavelet function ()t and is defined as
W(,)()() a b f t * t dt (8) ab, where
1 tb ab, ()t (9) a a
is the mother wavelet, ''a is the scaling factor, ''b is the shifting parameter and * denotes complex conjugation. The family of functions can be obtained by scaling and shifting of . The mother wavelet has the property that the set ()t forms an ab, ab, orthonormal basis in L2 () . This implies that the mother wavelet can, in turn, generate any function in . The mother wavelet has to satisfy the following admissibility condition:
()2 Cd (10) where, ()is the Fourier transform of . In practice, will have sufficient decay, so that the admissibility condition reduces to
(t ) dt (0) 0 (11)
The CWT has the drawbacks of redundancy and impracticability with digital computers. As the parameters a and b are the continuous values, the resulting CWT is a very redundant representation, and impracticable also. This impracticability is the result of the redundancy. Therefore, the scale and shift parameters are evaluated on a discrete grid of time-scale leading to a discrete set of continuous basis functions. The continuous inverse wavelet transform is discretised as
1/2 tb i f() t Wii a (12) i ai
Application of Wavelet Neural Networks… 135
To analyse discrete time-signals, it is convenient to take special values for ''a and ''b in defining this basis: if a 2 j and bn2 j (where j and n are integers) then, via translations and dilations:
j j/2 tn2 jn, (t ) 2 j (13) jn, 2
Eq. (13) forms a sparse orthonormal basis of L2 () [20]. This means that the wavelet basis induces an orthogonal decomposition of any function in . The applications of orthonormal wavelet bases and wavelet frames are usually limited to problems of small dimension. The main reason is that they are composed of regularly dilated and translated wavelet. For practical implementations, infinite basis and frames are always truncated. The number of wavelets in a truncated bases or frames drastically increases with the dimension, therefore, constructing and storing wavelet bases or frames of large dimension are prohibitive cost. In most practical situation of large dimension, the available data are sparse. If the inverse wavelet transform is discretised according to the distribution of the data, there are expectations to reduce the number of wavelets needed in the reconstruction. It is, thus, possible to handle problem of large dimension with such adaptive discretisation of the inverse wavelet transform. The adaptive discretisation consists of determining the parameters wi , ai and bi in Eq. 12 according to data sample (,)xy. This problem is very similar to neural network training. As a matter of fact, Eq. 12 can be viewed as a one hidden layer of neural network with as the activation function of the hidden neuron and with a linear neuron in the output layer. For this reason, we refer to the adaptively discretised inverse wavelet transform as wavelet network.
4.2. Wavelet Neural Networks
The idea of using wavelets in neural networks has been proposed by Zhang and Benveniste [21]. Zhang et al. described a wavelet-based neural network for function learning and estimation, and the structure of this network is similar to that of the RBF network except that the radial functions are replaced by orthonormal scaling functions [13]; Zhang presented wavelet network construction algorithms for the purpose of nonparametric regression estimation [22]. From the point of view of function representation, the traditional RBF networks can represent any function that is in the space spanned by the family of basis functions. However, the basis functions in the family are generally not orthogonal and are redundant. This means that the RBF network representation for a given function is not unique and is probably not the most efficient. Szu et al studied the adaptive wavelet transforms and gave several WNN topologies for approximation and classification of vocal signals [23]. Bakshi and Stephanopoulos creatively presented an orthogonal WNN for approximation and classification based on multi-resolution analysis [24]. Pati and Krishnaprasad studied the synthesis method of WNNs by which the number of hidden units might be determined based on the information from training data sets [25]. Unlike the multilayer perceptron which is a 136 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al. global network, WNN is a local network in which the output function is well localised in both time and frequency domains. In a local network only a small subset of weights are active at each point in the output space and the training of the network in one part of the input space does not corrupt that which has already been learnt in more distant regions. Thus, the learning speed of the local network is generally much faster than the global network. Two key problems in designing a WNN are how to determine architecture of the WNN and what learning algorithm can be effectively used for the training of the WNN. These problems are related to determining the optimal architecture of the WNN, to arranging the windows of wavelets, and to finding the proper orthogonal or non-orthogonal wavelet basis. Curse-of-dimensionality is a mainly unsolved problem in WNN theory, which brings some difficulties in applying the WNN to high-dimension problems [26]. At present, there are two different kinds of WNN structure, one with fixed wavelet bases, where the dilation and translation parameters of wavelet basis are fixed, and only the output layer weights are adjustable. Another type is the variable wavelet bases, where the dilation parameters, translation parameters and the output layer weights are adjustable. For WNNs with fixed wavelets, the main problem is the selection of wavelet bases/frames. The wavelet bases have to be selected appropriately since the choice of the wavelet basis can be critical to approximation performance. Recently, a node-configuration strategy to add new nodes gradually according to some simple thresholding rules has been presented [27]. It is well known that by using regularly truncated wavelet frames, the number of wavelet candidates would drastically increase with the dimension. Therefore, constructing and storing wavelet bases/frames for large dimension problems are of prohibitive cost. Obviously, to improve the approximation accuracy, large numbers of wavelet neurons are required for WNNs with fixed wavelet bases. This will result in a large complex network structure and cause over-fitting problems. For WNNs with variable wavelet basis, a new approach has first been presented by initialising the WNN as truncated wavelet frames, then followed by training with a backpropagation algorithm [28]. The fundamental concept of WNN is looking for a series of appropriate wavelet basis functions in wavelet space. This process can be realised by iterative computation of wavelet basis function, which is to make the energy function minimised. To design a WNN, the main work is to design the network structure, to determine the number of nodes in the hidden layer, and to choose the wavelet basis function and the learning algorithm of weights training. A simple WNN is shown in Figure 1. The wavelet network can be used as a nonparametric regression estimator. In nonparametric estimation there is a concern for the curse of dimensionality. This means that the complexity of the estimator grows quickly with input dimension. To address this concern, the following technique developed in [22] focuses on the sparse nature of the data associated with nonlinear functions. The notion of sparse data results from the fact that the functional mapping between the input and output is localised to a small portion of the input space. Exploiting this fact makes is possible to address problems of high input dimension.
Application of Wavelet Neural Networks… 137
Figure 1. WNN structure.
The WNN consists of three layers: input layer, hidden layer and output layer. The connections between input units and hidden units, and between hidden units and output units are called weights wti and Wt respectively. In this WNN, the training procedure is described as follows:
Initialising the dilation parameter at , translation parameter bt and node connection
weights wti , Wt to some random values. All those random values are limited in the interval (0, 1). T Input data Xin ()and the corresponding output values Vn , where i varies from 1 to S , representing the number of the input nodes, n represents the nth data sample of training set, and T represents the target output state.
The output value of the sample Vn is calculated with the following formula:
S T wti x n() i b t i1 VWnt (14) t1 at where is considered a mother wavelet, such as the Morlet wavelet filter which is shown in Figure 2, and is represented by
2 (t ) cos(0 t )exp( 0.5 t ) (15)
To reduce the error, , , , are adjusted using Wt , wti , at , bt . In the WNN, the gradient descend algorithm is employed, through the following equations,
E Wtt( j 1) W ( j ) (16) Wjt ()
138 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
E wtt( j 1) w ( j ) (17) wjt ()
E att( j 1) a ( j ) (18) ajt ()
E btt( j 1) b ( j ) (19) bjt () where the error function E is taken as:
N 2 1 T EVV nn (20) 2 n1 and, N standing for the data number of training set, and being the learning rate and the momentum term, respectively.
The process continues until satisfies the given error criteria, and the whole training of the WNN is completed.
Figure 2. Morlet Wavelet basis function.
5. WAVELET NEURAL NETWORKS WITH LINEAR COEFFICIENTS WEIGHTS
As has already been mentioned, two key problems in the design of WNNs are how to determine the WNN architecture and what learning algorithm can be effectively used for training the WNN. These problems are related to determining an optimal WNN architecture, to arranging the windows of wavelets, and to finding the proper orthogonal or non-orthogonal wavelet basis. In the standard form of WNN, the output of a WNN is given by Eq. 14, where Application of Wavelet Neural Networks… 139
th i is the wavelet activation function of i neuron of the hidden layer. For the n-dimensional input space, the multivariate wavelet basis function can be calculated by the tensor product of n single wavelet basis functions as follows
n ()()xx i (21) i1
The above WNN is a kind of basis function neural network in the sense that the wavelets consist of the basis functions. Note that an intrinsic feature of the basis function networks is the localised activation of the hidden layer units, so that the connection weights associated with the units can be viewed as locally accurate piecewise constant models whose validity for a given input is indicated by the activation functions.
Figure 3. WNN-LCW architecture.
Compared to the multilayer perceptron neural network (MLP), this local capacity provides some advantages, such as the learning efficiency and the structure transparency. However, the problem of basis function networks is also led by it. Due to the crudeness of the local approximation, a large number of basis function units have to be employed to approximate a given system. A shortcoming of the WNN is that for higher dimensional problems many hidden layer units are needed. In order to take advantage of the local capacity of the wavelet basis functions while not having too many hidden units, in this paper we propose an alternative type of WNN. The architecture of the proposed wavelet neural network-linear coefficients weights (WNN-LCW) is shown in Figure 3. In the proposed WNN-LCW structure, the connection weights between the hidden layer units and output units have been replaced by a local linear model. The usually used learning algorithm for WNN is gradient descent (GD) method. But its disadvantages are slow convergence speed and easy stay at local minimum. Therefore, in this research study, a hybrid GD and recursive least squares (RLS) training scheme has been employed. The proposed linear-weights scheme performs an operation similar to computing of the consequent in the fuzzy inference systems [29]. In addition, it has been proved that local linear models provide a more parsimonious interpolation in high-dimension spaces when modelling samples are sparse [30]. From Figure 140 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
3, the WNN approximates any desired signal y(t) by generalising a linear combination of a set of daughter wavelets m,n (t) which are generated by step sizes dilation and translation m and n from a mother wavelet with either of the following:
tn () m,n m (22) m m,n (2 t n) where m 0 . The structure of the output Layer 3, has been designed in the form of a local model f ( x12 ,x ,..xp ) , identical to the output layer appeared in Takagi-Sugeno-Kang (TSK) defuzzification schemes [31]. For modelling of dynamic processes, TSK schemes possess an excellent interpretation, which is superior to most, if not all, alternative approaches. The local modelling approach is based on a divide-and-conquer strategy. A complex modelling problem is divided into a number of smaller and thus simpler sub-problems. In addition it has been proved that local linear models provide a more parsimonious interpolation in high-dimension spaces when modelling samples are sparse [30]. Usually is a polynomial in the input variables x p but it can be any function as long as it can appropriately describe the output of the model within the fuzzy region specified by the antecedent of the rule. When is a first-order polynomial, the resulting fuzzy inference system is called a first-order Sugeno fuzzy model. Thus, the output of Layer 3 is given as
N y wj0 w j 1 x 1 ... w jp x p j ( x ) (23) j1 where, the network output is calculated as a weighted sum of the outputs of the local linear models and j is interpreted as the operating point dependent weighting factor (i.e. output of Layer 2). Low time consumption is an advantage of this system. The Sugeno-type system is usually used when we operate on numerical (crisp) data. This system can be viewed as switching regression model or mixture of local experts [32].The wavelet function adopted in hidden layer nodes is a modified differentiable version of Morlet wavelet which is shown in Figure 2, and is represented by
x 2 (x) cos(2 x)e (24)
This wavelet is derived from a function that is proportional to the cosine function and Gaussian probability density function. It is non-orthogonal and has infinite support [33]. Substituting Eq. 22 in Eq. 24, the activation function of jth wavelet node connected with the ith input data will be represented as Application of Wavelet Neural Networks… 141
xm ()j 2 n xm j (x ) cos(2 (ij ))e (25) mjj ,n i n j
A modified version (MWNN-LCW) of the above mentioned WNN-LCW is shown in Figure 4. Referring to Figure 4, Layer 1 accepts the input variables which are in form of T X [x,x1 2 ,...,x p ] , while Layer 2 is used to calculate the wavelet ―membership‖ values. In this layer, each node performs a membership function and acts as an element for membership degree calculation, where a wavelet function is adopted as the membership function. The nodes of Layer 3 are regarded as the ―wavelet‖ rules in association to the fuzzy rules in a neuro-fuzzy architecture. The number of the ―wavelet‖ membership functions for each input variable is equal to the number of ―wavelet‖ inference rules. These units are fixed, meaning that no modifiable parameter is associated with them. The multiplicative inference (Larsen product operator) has been used thus the output of this inference layer is given by
p (x ,x ,..,x ) (x ) j 1,2,..N i=1,2,..p (26) j 1 2 p mjj ,n i ii i1
The proposed approach differs from the conventional fuzzy rule table approaches [34]. In those models, an input space is divided into K12 K .... Kn fuzzy subspaces, where Ki , i12 , ,..,n is the number of fuzzy subsets for the ith input variable. There is a fuzzy rule for each of these subspaces. The main drawback of that approach is that the number of fuzzy rules increases exponentially with respect to the number of inputs n .
Figure 4. MWNN-LCW architecture.
In the tuning phase, emphasis has been given to the efficient optimisation of the network‘s parameters. A hybrid learning approach has been adopted. As the proposed architecture consists of linear and non-linear parts, a two-stage learning scheme, consisting of 142 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al. a recursive least-squares (RLS) and the gradient descent (GD) methods has been applied. This class of hybrid learning can speed up the learning process substantially and, simultaneously enhance its stability [35]. The network can be considered as a cascade of nonlinear systems and linear systems. In this phase, the error back-propagation (GD) is applied to tune the premise parameters of the wavelet functions and recursive least squares estimation is applied to find the consequence parameters of local linear systems. The hybrid-learning algorithm of WNN-LCW combines the recursive least-squares (RLS) method and the back propagation/gradient descent (BP/GD) to identify the parameters. The hybrid algorithm is composed of a forward pass and a backward pass.
1. RLS – In forward pass, signal is ―transmitted‖ toward last layer and the linear output weights are identified by RLS, while wavelet parameters remain fixed. The estimated linear parameters are known to be globally optimal [36]. 2. BP/GD – In backward pass we calculate the error signals recursively from the output layer backward to the hidden and input nodes. Thus the wavelet parameters are fine- tuned by GD.
6. MODEL VALIDATION
The proposed wavelet network, classic neural network approaches as well as statistical models were comparatively evaluated to determine whether they could successfully predict the responses of the pathogen at pressure levels other than those initially selected for model development. For this reason, two different high pressure levels, within the range employed to develop the models, were selected, namely 400 and 500 MPa. Additional milk pouches were prepared, inoculated with the pathogen and subjected to pressurisation as previously mentioned. At predetermined time intervals the surviving population of L. monocytogenes was enumerated and compared with the survival curves predicted by both the wavelet network and statistical models. The accuracy of the prediction was estimated by the calculation of the bias 2 (Bf) and accuracy (Af) factors, the regression coefficient (R ), the standard error of prediction (SEP), the mean absolute percentage error (MAPE) and the root mean square error (RMSE) [37].
7. NONLINEAR DYNAMIC SYSTEM IDENTIFICATION
In general, dynamic systems are complex and nonlinear. An important step in nonlinear systems identification is the development of a nonlinear model. In recent years, computational-intelligence techniques, such as neural networks, fuzzy logic and combined hybrid systems algorithms have become very effective tools of identification of nonlinear plants. The problem of identification consists of choosing an identification model and adjusting the parameters, such that the response of the model approximates the response of the real system to the same input. Application of Wavelet Neural Networks… 143
Since 1986, neural networks have been applied to the identification of nonlinear dynamical systems. Most of the works are based on multilayer feed-forward neural networks with backpropagation learning algorithm. A novel multilayer discrete-time neural network was presented in [38] for identification of nonlinear dynamical systems. In the framework of this research study, the proposed WNN-LCW structures will be utilised as a nonlinear models. Different methods have been developed in the literature for nonlinear system identification. These methods use a parameterised model. The parameters are updated to minimise an output identification error. A wide class of nonlinear dynamic systems with an input u and an output y can be described by the model:
ymm( k ) f ( k ), (27)
where, ykm () is the output of the model, ()k is the regression vector and is the parameter vector. Depending on the choice of the regressors in , different models can be derived:
NARX (Nonlinear AutoRegressive with eXogenous inputs) or series-parallel model:
(k ) uk ( 1), uk ( 2),...., uknyk ( uy ), ( 1), yk ( 2),..., ykn ( ) (28)
where nu denotes the maximum lag of the input, while ny denotes the maximum lag of the output.
NOE (Nonlinear Output Error) or parallel model:
(k ) uk ( 1), uk ( 2),...., uknyk ( u ), m ( 1), yk m ( 2),..., ykn m ( y )
The NARX and NOE models (Figures 5a and 5b) are the most important representations of nonlinear systems.
Figure 5a. NARX model. 144 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
Figure 5b. NOE model.
For identification we consider the case where the plant is observable and where input- output measurements are available. Dependent on the kind of inputs used, either parallel or series-parallel models can be utilised.
Parallel model: the output of the model itself is used to create the time-lagged inputs. This model can be considered as a fully recurrent model. The parallel model is able to give predictions over a short period of time. The model is said to have internal dynamics. Series-parallel model: the outputs of the actual system are used as inputs to the model. Only a one-time ahead prediction is possible. The model is said to have external dynamics.
In both cases the prediction error of the model, compared with the true plant outputs are used as a measure to optimise the model parameters. For dynamic systems, the model must have some way to implement time lags. In other words, some memory function must be present in the model. In modelling using computational intelligence schemes, such as neural networks, neuro-fuzzy systems, WNNs, this can be done in two ways: either, delayed inputs and outputs are used as extra external inputs, or some memory is included in the individual neurons. Models with external dynamics can be seen as one-step-ahead predictors. Models with internal dynamics are best used for simulation purposes, as the model doesn‘t need the true plant outputs. The latter case has a higher potential for output errors in the long term. This is certainly the case for nonlinear systems, where the internal nonlinearities can drive the system into a chaotic state. Since for nonlinear problems the complexity usually increases strongly with the input space dimensionality (curse of dimensionality) the application of lower dimensional NARX or NOE models is more widespread. One drawback of these models is that the choice of the dynamic order, ny , is crucial for the performance and really efficient methods for its determination are not available. Often the user is left with a trial-and-error approach. However the main advantage of the NARX and NOE models against those models without output feedback (such as Nonlinear Finite Impulse Response - NFIR) is that they have a very compact description of the process. As a consequence, the regression vector ()k contains only a few entries and thus the input space for the approximation f is Application of Wavelet Neural Networks… 145 relatively low dimensional. Such an advantage is even more important when dealing with nonlinear than with linear systems.
8. DISCUSSION OF RESULTS
The objective of this study was to investigate the feasibility of using a WNN scheme for the development of a series-parallel model of survival curves of Listeria monocytogenes under high pressure treatment in whole UHT milk. The survival curves of Listeria monocytogenes inactivated by high hydrostatic pressure were obtained at six pressure levels (350, 400, 450, 500, 550 and 600 MPa) in UHT whole milk (Figure 6). Interestingly, the shapes of the survival curves that follow those experimental data change considerably depending on the treatment pressure levels. However, in all pressure levels assayed, a clear inactivation pattern was observed including a lag phase (or shoulder), a log-linear and a tailing phase. As expected, the duration of shoulder was pressure dependent, so higher pressures resulted in lower shoulder time. At different pressure levels, survival curves showed a pronounced curvature and tailing indicating that a small population of the pathogen could resist pressurization and eventually survive in milk.
Figure 6. Illustration of the experimental data.
The estimated kinetic parameters of inactivation based on the models of re-parameterized Gompertz and modified Weibull are presented in Table 1. All models fitted the experimental data well as can be inferred by the high values of regression coefficient (R2 > 0.97) and low values of root mean square error (RMSE < 0.45). Figure 7 (a and b) illustrates the models‘ performance on the training data. Since experimental error cannot account for the nonlinearity frequently encountered in survival curves, many explanations have been proposed, such as the variability of sensitivities to lethal agents in the bacterial population, mixed bacterial population in samples where each of its component has a first-order inactivation kinetics, inactivation kinetics of a different order or a different kind of kinetics, or adaptation to the stress that makes the remaining cells more resistant. However, there are still no satisfactory explanations for the phenomena of shoulder and tailing.
146 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al.
Table 1. Parameter estimationa and statistical indices of the different models used for fitting the survival of L. monocytogenes in whole UHT milk during high pressure treatment
2 Model log10 log10 log10 kmax ts δ p RMSE R b - type N0 A Nres [min [min] [min] [-] [CFU [CFU [CFU 1] ml-1] ml-1] ml-1] Gompertz 350 MPa 6.90 ± 4.08 ± 0.41 ± 10.07 0.307 0.969 0.16 0.59 0.03 ± 2.51 450 MPa 6.99 ± 4.15 ± 0.39 ± 2.25 ± 0.313 0.971 0.42 0.69 0.04 0.16 550 MPa 7.36 ± 6.21 ± 1.68 ± - c 0.432 0.987 0.32 0.18 0.17 600 MPa 6.51 ± 6.30 ± 2.26 ± - 0.311 0.993 0.58 0.96 0.16 Weibull 350 MPa 7.00 ± 3.27 ± 14.53 ± 1.88 0.266 0.977 0.15 0.25 1.88 ±0.40 450 MPa 6.94 ± 3.09 ± 7.71 ± 1.13 ± 0.318 0.970 0.24 0.25 2.14 0.28 550 MPa 6.74 ± 0.61 ± 2.05 ± 1.24 ± 0.4136 0.988 0.37 0.48 0.74 0.34 600 MPa 6.41 ± 0.65 ± 1.46 ± 1.11 ± 0.102 0.999 0.10 0.10 0.14 0.07 Wavelet 350 MPa 0.168 0.994 450 MPa 0.249 0.986 550 MPa 0.200 0.995 600 MPa 0.110 0.998 a Data are values ± standard deviation. b A is the difference between the initial population (N0) and the residual population (Nres). c No shoulder was observed.
The prediction capability of those models was considered by adopting a two-step standard procedure commonly applied in predictive microbiology [39]. Initially, the primary models (i.e., Gompertz, Weibull) were fitted to high pressure inactivation data and the respective kinetic parameters were calculated (Table 1). Subsequently, the derived kinetic parameters were related to high pressure levels through the development of first or second order secondary polynomial models and their new estimates were determined at 400 and 500 MPa, which have been pre-selected for model validation. For the kinetic parameters which did not present a clear trend with pressure, their respective values at 400 and 500 MPa were determined by interpolation. Finally, based on the new values of the kinetic parameters at the selected pressures for validation, equations 1 and 2 were refitted and compared with survival data of the pathogen at the same pressures, in order to determine the potential of the models for generalisation, i.e., their ability to foresee survival curves at pressures for which there was no previous training. The performance against the unknown 400MPa and 500MPa curves is illustrated in Figures 8 and 9, respectively. Application of Wavelet Neural Networks… 147
Figure 7: Survival curves of Listeria monocytogenes in UHT whole milk during high pressure processing at 350 MPa (), 450 MPa (▲), 550 MPa (), and 600 MPa (■), generated by the reparameterized Gompertz model (a), the modified Weibull model (b), and the wavelet neural network (c). Data points are mean values of two independent experiments with two replications each.
Figure 8. Observed values and predicted survival curves of Listeria monocytogenes in UHT whole milk during high pressure treatment at 400 MPa, generated by the reparameterized Gompertz model (a), the modified Weibull model (b), and the wavelet neural network (c). Data points are mean values of two independent experiments with two replications each.
Determination of kinetic parameters during microbial inactivation, whether through high pressure, chemical or other agents is a rather complicated task. Particularly in the case of non- thermal inactivation caused by adverse environmental conditions, the shape of survival curves indicates more pronounced heterogeneity according to the intensity of the stress and may also 148 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al. vary with the physiological state of the cells, the phase of growth (exponential or stationary), and the conditions of adaptation before the stress.
Figure 9. Observed values and predicted survival curves of Listeria monocytogenes in UHT whole milk during high pressure treatment at 500 MPa, generated by the reparameterized Gompertz model (a), the modified Weibull model (b), and the wavelet neural network (c). Data points are mean values of two independent experiments with two replications each.
Consequently, concave curves may become convex or sigmoidal when the intensity of the stress varies. It must be pointed out that despite the plethora of proposed inactivation models in the literature none is flexible enough to account for all changes of shapes with the intensity of stress. The selected models were able to describe the survival of the pathogen at 350, 450, 550, and 600 MPa quite accurately. However, the prediction at 400 and 500 MPa was not very accurate as the experimental values of the pathogen showed a pattern which possibly indicated the presence of two subpopulations, one sensitive to high pressure that was inactivated within the first 10 min of the process (Figures 7 and 8) and a second more resistant to the applied stress. The discrepancy observed in the prediction at these pressure levels could be attributed to the fact that both models did not account for the presence of a mixed population of the pathogen with a variable resistant to high pressure. Small data set conditions exist in many fields, such as food analysis, disease diagnosis, fault diagnosis or deficiency detection in mechanics, aviation and navigation, etc. The main reason that small data sets can not provide enough information as that of large ones is that there exist gaps between samples; even the domain of samples can not be ensured [40]. It is hard to catch the pattern of high order non-linear functions by a standard feed-forward neural network-like scheme, with a small sample set, since they have shown weakness in providing sufficient information for forming population patterns. Lacking the whole picture of a function means the network cannot precisely identify which sections of the function are ascending and which sections are descending. Hence, for learning systems that lack sufficient data, the knowledge learned is often unacceptably rough or unreliable. Application of Wavelet Neural Networks… 149
How to fill up the gaps is the primary problem to be solved. Inspired by the way the RBF network approximates a nonlinear function through Gaussian local-basis functions, we employed such a network to each ―survival curve‖ defined from the experimental data. The aim was to associate each local-basis-function to each sample, and therefore easy then to generate new data that satisfy each ―survival curve‖. An RBF network using the regularised orthogonal least squares learning algorithm has been employed for this task [41]. An RBF is a function which has in-built distance criterion with respect to a centre. Because a fixed centre corresponds to a given regressor in a linear regression model, the selection of RBF centres can be regarded as a problem of subset selection. The orthogonal least squares (OLS) method can be employed as a forward selection procedure that constructs RBF networks in a rational way. The algorithm allows the selection of the centres one by one in a rational procedure, each selected centre maximises the increment to the explained variance of the desired output. For each pressure level case, an RBF network has been associated. As the real number of samples for each pressure level is very limited, we associated each RBF centre with the real samples. Then with a constant time-step 0.5 min, through a 2-inputs network, a ―continuous survival curve‖ has been obtained for each pressure level. The inputs included the type of pressure level and the sampling time-step, while the output was related to the bacteria counts. Each ―continuous survival curve‖ has been verified against the real experimental samples. Based on these continuous datasets, the capabilities of proposed WNN-LCW architectures have been verified as a one-step-ahead prediction system. Comparative studies have been conducted with the utilisation of a PLS regression model and an MLP neural network. Pressure levels of 400 MPa and 500 MPa have been used as testing datasets, while the remaining levels as training ones. Following the principles of nonlinear identification, NARX models using the WNN- LCW, the MLP and the PLS schemes have been developed. The training dataset, consisting of 204 data from 350, 450, 550, and 600 MPa ―continuous survival curves‖, was employed, while 81 data from 400MPa and 51 data from 500MPa curves were kept for validation. During trials, it has been found that the model is sensitive to the previous number of bacteria counts, proving thus its dynamic behaviour. In the proposed WNN-LCW, 25 wavelet Morlet functions have been used, while the network‘s learning parameter vector was [ m , n , v , ] [0.001,0.17,0.17,0.2]. The hybrid parameter learning algorithm has been utilised, which resulted a high speed training process, i.e. 10 epochs. Figure 7c shows the performance of the WNN model, especially against the real experimental points from the training survival curves. The fitting performance of the developed WNN was comparable with the statistical models based on the comparison of the same indices (Table 1), as the root mean square error index ranged from 0.110 to 0.249, while the values of R2 were also high (0.986-0.988). The high fitting performance of the WNN approach was expected as the network has been trained on these particular datasets. Results showed that the WNN was more effective in predicting the response of the pathogen compared with statistical models as illustrated by graphical plots (Figures 8, 9) implying that although the WNN has been trained on different survival curves, it has managed to learn the underlying process with high accuracy. In a similar way, an MLP neural network using the classic backpropagation learning algorithm was constructed with the same input structure as WNN. Through trial and error, eventually two hidden layers with 12 and 8 nodes respectively have been employed. The 150 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al. learning algorithm was responsible for the network‘s slow convergence, which took approximately 30,000 epochs. Figures 10 and 11 illustrate the MLP performance for both testing survival curves.
Figure 10. Survival curves of Listeria monocytogenes during high pressure treatment at 400MPa fitted with different modelling schemes.
Figure 11. Survival curves of Listeria monocytogenes during high pressure treatment at 500MPa fitted with different modelling schemes.
The PLS-NARX scheme was certainly much more accurate from the previous simple PLS case. Like WNN and MLP, the PLS regression model was constructed to anticipate the dynamic nature of the specific problem, by including past values of the Listeria counts as inputs. The calculated by XLSTAT software, equation has the following form
Y..X.X.X.X.X.X10 1646857 0 000172 1 0 0055 2 0 0055 3 0 0055 4 0 955 5 1 9412 6 (30)
Figures 10 and 11 illustrate the PLS performance for both testing survival curves. With regard to the assessment of the quality of the overall model predictions various statistical criteria were calculated at all the tested validation experiments. Application of Wavelet Neural Networks… 151
The regression coefficient ( R2 ) is often used as an overall measure of the prediction attained. It measures the fraction of the variation about the mean that is explained by a model. The higher the value ( 01R2 ), the better is the prediction by the model [42]. The wavelet neural networks developed herein were found to yield better agreement with experimental observations for the test data set compared to data predicted by the MLP and the PLS model. The values of the coefficient of determination ( ), as shown in Table 2, indicate a very good fit of the experimental data from the WNN-based approaches. However, is a suitable criterion for model comparison on the assumption that the error is normally distributed and not dependent on the mean value; In fact, the distribution of the error is not clearly known in the case of microbial/bacteria growth, so this term must be used with caution, particularly in non-linear regression models and hence additional indices must be employed for model comparison. The RMSE values of the WNNs were also significant better for the two ―test‖ survival curves, i.e. 400MPa and 500MPa. This index is calculated between the desired and output values and then averaged across all data and it can be used as an estimation of the goodness of fit of the models.
Table 2. Performance Indices
Statistical index Model Testing Data sets 400MPa 500MPa Coefficient of determination (R2) MLP 0.9526 0.9933 WNN-LCW 0.9935 0.9996 PLS 0.9796 0.9966 MWNN-LCW 0.9985 0.9999 Root mean square error (RMSE) MLP 0.3830 0.1733 WNN-LCW 0.1627 0.1128 PLS 0.5532 0.2246 MWNN-LCW 0.0670 0.0198 Mean absolute percentage error (MAPE) MLP 23.2939 2.7674 (%) WNN-LCW 5.3072 2.0750 PLS 32.8082 5.3354 MWNN-LCW 2.0035 0.3659 Mean Square Error MLP 0.1467 0.0300 WNN-LCW 0.0265 0.0127 PLS 0.3061 0.0504 MWNN-LCW 0.0045 0.0003912 Standard error of prediction (SEP) (%) MLP 15.9921 3.9301 WNN-LCW 6.7934 2.5569 PLS 23.1004 5.0930 MWNN-LCW 2.7967 0.4485 Bias factor (Bf) MLP 1.0177 0.9751 WNN-LCW 0.9750 0.9792 PLS 1.2960 1.0491 MWNN-LCW 1.0137 0.9982 Accuracy factor (Af) MLP 1.2182 1.0288 WNN-LCW 1.0551 1.0212 PLS 1.2983 1.0525 MWNN-LCW 1.0198 1.0037
It can also provide information about how consistent the model would be in the long run. The RMSE values for both networks (WNN and MLP) were lower those from the linear PLS 152 V. S. Kodogiannis, M. Amina, J. N. Lygouras et al. model, indicating the ability of non-linear networks to make better predictions on data for which there was no previous training. The benefits of mathematical models to predict pathogen growth, survival and inactivation in foods include the ability to account for changes in microbial load in food as a result of environment and handling; the use of predictive microbiology in management of foodborne hazards; and, the preparation of Hazard Analysis Critical Control Point (HACCP) plans. The usual measures of goodness-of-fit for model comparison in food microbiology is 2 performed by calculating in addition to squared correlation coefficient ( R ) the bias ( B f ) and accuracy ( Af ) indices as proposed by Ross [37] and results are shown in table 2.
CONCLUSION
In conclusion, the survival curves of L. monocytogenes in UHT whole milk could have different shapes depending on the treatment pressure levels. The development of accurate mathematical models to describe and predict pressure inactivation kinetics of microorganisms, such L. monocytogenes should be very beneficial to the food industry for optimisation of process conditions and improved dependability of HACCP programs. In this research study we have developed a new type of wavelet neural network, and validated it in an NARX identification scheme for the modelling of the Listeria monocytogenes survival/death curves. Results and comparison with classical statistical methods have revealed a very good accuracy which was accompanied with a high speed training process.
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Chapter 7
THE EFFECTS OF NATURAL ANTIOXIDANTS DIETARY SUPPLEMENTATION ON THE PROPERTIES OF FARM ANIMAL PRODUCTS
Panagiotis E. Simitzis and Stelios G. Deligeorgis Department of Animal Breeding and Husbandry, Faculty of Animal Science and Aquaculture, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
ABSTRACT
Animal products are important sources for protein, fat, essential amino acids, minerals, vitamins and other nutrients in human nutrition. In recent years, the consumer demands for healthier animal products with favorable properties and improved quality are rapidly increasing worldwide. Antioxidants represent a group of compounds effective in improving quality characteristics of animal products by limiting the negative implications of lipid oxidation. Oxidation of lipids and the production of free radicals are natural processes occurring in biological systems leading to oxidative deterioration. Oxidative deterioration is initiated in the highly-unsaturated fatty acid fraction of membrane phospholipids, leading to the production of hydroperoxides, which are susceptible to further oxidation or decomposition to secondary reaction products such as short-chain aldehydes, ketones and other oxygenated compounds that may adversely affect lipids, pigments, proteins, carbohydrates vitamins and the overall quality of animal products by causing loss of nutritive value and limiting shelf-life. Oxidation destroys the membrane structure, disturbs transport processes and causes losses in the function of the cell organelles. In the past synthetic antioxidants such as butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), tert-butylhydroquinone (TBHQ), gallates were extensively used with the intention to delay, retard or prevent the negative effects of lipid peroxidation by scavenging chain-carrying peroxyl radicals or diminishing the formation of initiating lipid radicals. During the last decades interest in employing antioxidants from natural
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sources to increase the shelf life of foods is considerably enhanced by consumer preference for natural occurring ingredients and concerns about the possible toxic effects of synthetic antioxidants. The majority of natural antioxidants are phenolic compounds and the most important groups of them are the tocopherols, flavonoids and phenolic acids. The traditional practice of adding antioxidants during processing can still play a very important role since the added compounds have the potential for enhancing the activity of the inherent antioxidants systems. However, the dietary supplementation with antioxidants appears to be a more effective way of retarding lipid oxidation of animal products and controlling stability compared to post mortem addition of antioxidants. Dietary supplementation has been proved to be a simple and convenient strategy to uniformly introduce a natural antioxidant into phospholipid membranes where it may effectively inhibit the oxidative reactions at their localized sites. As a result, components of natural antioxidants are distributed, retained, and remained functional in animal products. On the other hand, no negative implications on animal products quality properties have been observed. Nowadays, there is a strong interest in isolating antioxidants from natural sources and incorporating them in animal nutrition with the intention to minimize lipid oxidation in products and to enhance their quality and nutritive value. However, further study is needed to elucidate their exact action and establish their regular use in animal production.
INTRODUCTION
Animal products are considered as very nutritive foods, since they provide human organism with high quality proteins, vitamins and minerals. However, autoxidation of lipids and the production of free radicals are natural processes occurring in biological systems leading to oxidative deterioration and off-flavors development. Lipid oxidation is a free- radical chain reaction that causes a total change in the sensory properties and nutritive value of animal products (Jadhav et al., 1996). In recent years, global epidemiological studies and several research approaches have been targeted at uncovering the causes and modifying factors associated with the etiology of several important diseases affecting human health. Much attention has been therefore paid to develop animal products with physiological functions to promote human health conditions and prevent the risk of diseases. Enrichment of products with bioactive compounds, such as antioxidant agents appears to improve product quality and protect consumer health against oxidation. Furthermore, the dietary supplementation with antioxidants seems to be a more effective way of retarding lipid peroxidation of animal products and controlling its stability compared to post mortem addition of antioxidants (Zhang et al., 2010).
FREE RADICALS – LIPID OXIDATION
A free radical is any species, atoms, molecules or any compounds, capable of independent existence that contains one or more unpaired electrons. The presence of one or more unpaired electrons makes the species highly reactive and unstable. Radicals can be formed by the loss of a single electron from a non-radical, or by the gain of a single electron by a non-radical. They can easily be formed when a covalent bond is broken if one electron The Effects of Natural Antioxidants Dietary Supplementation … 157 from each of the pair shared remains with each atom, a process known as homolytic fission. The loss of electrons by an atom or molecule is known with the term oxidation (Halliwell and Gutteridge, 1996). Free radicals are formed as a natural consequence of the organism‘s normal metabolic activity in tissues and can have damaging effects on biologically relevant molecules such as DNA, proteins, lipids and carbohydrates. It is generally accepted that the superoxide radical is the main free radical in living cells and the electron transport chain in the mitochondria is considered to be responsible for the superoxide anion formation (Haliwell and Gutteridge, 1996). The formation of free radicals is mediated by a number of agents and mechanisms such as high oxygen tension, heat, radiation or sensitizers (metals or enzymes). The free radicals produced are highly reactive with molecular oxygen, forming peroxy radicals and hydroperoxides. Hydroperoxides are involatile and odorless, but relatively unstable compounds that they decompose to aldehydes and ketones to form volatile aroma compounds (off-flavors). For example in meat products, oxidation manifests as a conversion of the red muscle pigment myoglobin to brown methmyoglobin and the development of rancid odors and flavors from the degradation of the polyunsaturated fatty acids in the tissue membranes (Jadhav et al., 1996). Lipids can be classified into three groups: (1) simple lipids (triglycerides, steryl esters and wax esters), (2) compound lipids (phospholipids, glycolipids, sphingolipids and lipoproteins) and (3) derived lipids (fatty acids, fat-soluble proteins and provitamins, sterols, terpenoids and ethers). Phospholipids are more susceptible to oxidation than triglycerides and cholesterol esters because of their high content of polyunsaturated fatty acids. Unsaturation of fatty acids makes lipids susceptible to oxygen attack leading to complex chemical processes, known with the term ―autooxidation‖. The first stage of the above processes is called the initiation phase, during which carbon-centered free radicals are produced from a precursor molecule, for example a polyunsaturated fatty acid (PUFA). These relatively unreactive radicals react with the oxygen producing highly reactive peroxyl radicals starting the next stage of lipid peroxidation called the propagation phase. The resulting peroxyl radical can attack any available peroxidazable material capable of peroxidation producing hydroperoxide and a new carbon-centered radical. As it is pointed out, lipid peroxidation is a chain reaction and many cycles of peroxidation could cause severe damage to cells, since it leads to the formation of various products that can negatively influence cellular function (Gordon, 2001). It is widely accepted that most human diseases at different stages of their development are associated with free radical production and metabolism. Although lipid peroxidation products have cytotoxic, mutagenic, carcinogenic, atherogenic, and angiotoxic effects, these mechanisms still remain unclear. Free radicals are implicated in the initiation or progression phase of various diseases, including cardiovascular disease (coronary heart disease, atherosclerosis), some forms of cancer (breast, prostate, pancreas, esophagus, stomach, colon, etc), cataracts, aging process etc (Deshpande et al., 1996). In animal cellular membranes the major substrates for peroxidation are the PUFAs, compounds that are necessary for maintenance of physiologically important membrane properties including fluidity and permeability (for example docosahexaenoic and arachidonic acid). PUFAs susceptibility to peroxidation is proportional to the amount of double bonds in the molecules. Oxidative deterioration is initiated in the highly-unsaturated fatty acid fraction of membrane phospholipids, leading to the production of hydroperoxides, which are 158 Panagiotis E. Simitzis and Stelios G. Deligeorgis susceptible to further oxidation or decomposition to secondary reaction products such as short-chain aldehydes, ketones and other oxygenated compounds that may adversely affect lipids, pigments, proteins, carbohydrates vitamins and the overall quality of animal products by causing loss of color and nutritive value and limiting shelf-life. Lipid peroxidation destroys the membrane structure, disturbs transport processes and disrupts the function of the cell organelles (Jadhav et al., 1996). Many factors seem to affect lipid peroxidation in animal tissues: (1) species, (2) sex, (3) age, (4) anatomical location, (5) diet, (6) environmental temperature, (7) light, (8) exposure to air, and (9) phospholipid composition and content. On the other hand, various biochemical components involving trace minerals, enzymes and vitamins protect the cellular structure and function from oxidative damage (Gordon, 2001).
ANTIOXIDANTS
Living organisms have developed specific mechanisms to protect cells from the actions of free radicals. Protective antioxidant compounds are located in organelles, subcellular compartments or the extracellular space creating the organism ―antioxidant system‖. Antioxidant enzymes (glutathione peroxidase, catalase and superoxide dismutase), water soluble antioxidants (ascorbic acid etc), fat soluble antioxidants (vitamin A, vitamin E, carotenoids etc) and the thiol redox system consisting of the glutathione and the thioredoxin system (supply of electrons for deoxyribonucleotide formation, antioxidant defense and redox regulation of signal transduction, transcription, cell growth and apoptosis) enable the maximum cellular protection from free radicals by providing three levels of defense (Arner and Holmgren, 2000; Gordon, 2001). The first level consists of the antioxidant enzymes (glutathione peroxidase, catalase and superoxide dismutase) and metal-binding proteins (transferrin, haptoglobin, ferritin etc) that functions by removing precursors of free radicals or by inactivating catalysts leading to the prevention of first chain initiation. Unfortunately the first level is not sufficient to completely prevent free radical formation. Some peroxyl radicals escape and several chain breaking antioxidants (vitamin E, vitamin A, carotenoids, ascorbic acid etc) inhibit peroxidation by scavenging peroxyl radical intermediates and by keeping the chain length of the propagation reaction as small as possible (second level of antioxidant defense in the cell). However, even the second level is not able to prevent lipid peroxidation and the integrity of the biological molecules is damaged. In this case, the third level of antioxidant defense is activated and eliminates or repairs the damaged molecules. This level includes lipolytic, proteolytic and other enzymes. The cooperative interaction between the above antioxidants that constitute the three levels of antioxidant defense in the cell is vital for maximum protection from the deleterious effects of free radicals and the maintenance of the critical balance among antioxidant defense, repair systems and free radicals (Haliwell and Gutteridge, 1996; Rajalakshmi and Narasimhan, 1996). The internal antioxidant system is drastically influenced by exogenous factors. For example, natural antioxidants (vitamins, carotenoids etc) and optimal levels of several elements (Mn, Cu, Zn, Se etc) in animal diet contribute in the maintenance of efficient antioxidant levels in animal tissues. On the other hand, stress conditions of environmental (increased temperature, humidity etc) or nutritional (high levels of PUFAs, toxicants, The Effects of Natural Antioxidants Dietary Supplementation … 159 deficiencies of vitamins or elements etc) origin are associated with increased production of free radicals. In detail, a decrease of coupling of oxidation and phosphorylation in the mitochondria is observed that causes an increased electron leakage and overproduction of superoxide radical. Cells can usually tolerate mild oxidative stress by additional synthesis of antioxidants in an attempt to restore the critical balance between antioxidants and free radicals. When the free radical generation exceeds the ability of the oxidative system to neutralise them, damages are caused to unsaturated lipids in cell membranes and cell integrity is disrupted (Halliwell and Gutteridge, 1996). Endogenous antioxidants of animal products are often lost during processing, handling or storage of products, necessitating the further supplementation with exogenous antioxidants. In general, antioxidants are capable of donating hydrogen radicals and effectively minimize rancidity, retard lipid peroxidation, without any damage to sensory or nutritional properties of meat products, resulting in maintaining of quality and enhancing of shelf life. Moreover, antioxidants do not impart foreign color, odor, or flavor and are stable to heat processing, easy to incorporate and effective at low concentrations (Yanishlieva-Maslarova, 2001). In the past synthetic antioxidants such as butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), tert-butylhydroquinone (TBHQ), gallates were extensively used with the intention to delay, retard or prevent the negative effects of lipid peroxidation by scavenging chain-carrying peroxyl radicals or diminishing the formation of initiating lipid radicals (primary or chain-breaking and secondary or preventive antioxidants, respectively). Antioxidants function mainly by disrupting the free-radical chain reaction or by decomposing the lipid peroxides formed into stable end products. Due to concern over the safety of synthetic compounds, extensive work is being carried out the last decades to find novel naturally occurring compounds as alternatives for the synthetic antioxidants. As a result, there is a strong tendency towards isolating organic antioxidants from natural sources for the protection of product quality and consumer health against oxidation. The majority of natural antioxidants are phenolic compounds and the most important groups of natural antioxidants are the tocopherols, flavonoids and phenolic acids (Shi et al., 2001). In the last decades, research on natural occurring antioxidants has been developing very quickly. Increased antioxidant dietary supplementation is shown to be beneficial in many cases, including immunostimulation, prevention of a decrease in productive and reproductive performances in various stress conditions etc. Natural antioxidants are regarded as compounds capable of delaying, retarding or preventing autoxidation processes and act at different levels in the oxidative sequence involving lipid molecules. They may decrease oxygen concentration, intercept singlet oxygen, prevent first-chain initiation by scavenging initial radicals such as hydroxyl radicals, bind metal ion catalysts, decompose primary products of oxidation to nonradical species and break chains to prevent continued hydrogen abstraction from substrates. At the same time antioxidants could preserve the integrity of cell membranes by preventing the oxidation of membrane phospholipids during storage which inhibits the passage of sarcoplasmic fluid through the membrane (Shi et al., 2001). Current intensive farming conditions are associated with stressful conditions that stimulate the increased production of free radicals. It is therefore very important to provide the necessary requirements of the main antioxidant nutrients through the diet. Antioxidants act at the molecular and cellular level by having an important role in gene expression and regulation, apoptosis and signal transduction (Frei, 1999). All antioxidants in the animal body 160 Panagiotis E. Simitzis and Stelios G. Deligeorgis are working together building a so called ―antioxidant system‖ responsible for prevention of damaging effects of free radicals and toxic products of their metabolism.
VITAMIN E
The most widely accepted physiological function of vitamin E is its role as a scavenger of free radicals (hydroxyl, alkoxyl, peroxyl, and superoxide anion radicals). However, the major role of vitamin E in vivo is to quench chain carrying lipid peroxyl radicals to break the chain propagation of lipid peroxidation in the biological membranes. The antioxidant activity of vitamin E is mainly due to its ability to donate its phenolic hydrogen to lipid free radicals. In detail, presence of vitamin E in the biological membranes contributes in: (a) antioxidant protection by free radical scavenging, (b) structural stabilization of phospholipid polyunsaturated fatty acyl residues, preventing breakdown of endogenous phospholipids, (c) stabilization of phospholipid bilayers against disturbance by PUFA and (d) modulation of membrane associated enzyme systems (Surai, 2003). In nature, only plants can synthesize vitamin E in the forms of tocopherols and tocotrienols, with a-tocopherol being the most known form. Tocopherols are present in oil seeds, leaves, and other green parts of plants and tocotrienols are widely distributed in the bran and germ fractions of seeds and cereals. Vitamin E level varies in plants depending on crop location, fertilization, environmental and harvesting conditions (Madhavi et al., 1996). It is generally assumed that vitamin E absorption follows the general principles of lipids absorption and metabolism. Feed derived vitamin E is consumed mainly in the free alcohol form. After the ingestion of feed, tocopherols and tocotrienols are released from the matrix by digestive enzymes. Free fatty acids and monoglycerides derived during hydrolysis of triglycerides, in the presence of bile salts and phospholipids (emulsification), spontaneously form very small particles, called mixed micelles (solubilisation). The absorption and assimilation from the diet continues with diffusion across the unstirred water layer, permeation through the membrane of the enterocytes, incorporation into lipoprotein particles and release into the circulation. As it is concluded, micelles formed from dietary lipids serve as a delivery system for vitamin E to reach the absorptive surface of the gut. However, the efficacy of vitamin E dispersion from feed to animal organism appears to be influenced by the amount and type of feed components (vitamin E bioavailability), its interaction with specific nutrients (vitamin A, vitamin C, metals, dietary oils etc), and the general nutritional status of the animal. At the same time, the presence of fatty acids of varying chain length and the degree of saturation also influence the absorption rate (Hollander, 1981). The major vitamin E sources in the diet are vegetable sources: wheat, soybean, sunflower and corn. At the same time, vitamin E is mainly accumulated in adipose tissue, liver and muscle (Drevon, 1991). However, the vitamin E reserves that are accumulated in adipose tissue do not rapidly respond to vitamin E deficiency and their mobilization is very slow (Bjorneboe et al., 1990)
The Effects of Natural Antioxidants Dietary Supplementation … 161
Carotenoids
Carotenoids constitute the most numerous and widespread group of pigments in nature and are responsible for various bright colors in flowers, fruits, vegetables etc. Biosynthesis of carotenoids (lutein, zeaxanthin, b-carotene, b-cryptoxanthin etc) takes place only in plants and their concentration in feed ingredients varies substantially. In general, carotenoids protect against the potentially damaging combination of oxygen, light and photosensitizing molecules by quenching both the triplet excited states of the photosensitisers and the singlet excited oxygen molecules and preventing the formation of hydroperoxides (Armstrong, 1997). They are associated primarily with the lipid portions of animal tissues and cells, including membranes and cytoplasmic lipid droplets. In bilayer membranes carotenoids act as modulators of membrane phase transition, fluidity, polarity and permeability (Socaciu et al., 2000). Feed derived carotenoids are found in the free alcohol form or as esterified forms. Carotenoid absorption can be divided into four stages: digestion of the feed matrix, formation of lipid-mixed micelles, uptake of carotenoids into the intestinal mucosal cells and delivery to the plasma circulation. As with vitamin E, the amount and quality of dietary fat have major influences on the efficiency of carotenoid absorption (bile secretion and micelle formation). Absorption kinetics and plasma transport also seem to differ among carotenoids, possibly because of differences in polarity (Southon and Faulks, 2001). Antioxidant potency of carotenoids is determined by several factors, including oxygen tension, carotenoid concentration, and interactions with other antioxidants (Palozza, 1998). Carotenoids are efficient quenchers of O2 and also effective scavengers of free radicals, when they are incorporated into tissues in the correct location and at a suitable concentration. Furthermore, they modulate antioxidants systems of the body by interacting with other antioxidants including vitamin E (tocopherols). Farm animals with yellow fat such as cattle effectively accumulate carotenoids in adipose issue, whereas white fat animals such as rabbits, pigs and sheep do not accumulate carotenoids in their adipocytes (van Vliet, 1996).
Phenolics
Active components from various spices and herbs such as rosemary, sage, thyme, oregano, pepper, and cloves possess antioxidant properties and have been extensively used for their preserving ability for hundreds of years. Phenolics can be defined as substances possessing an aromatic ring bearing one or more hydroxyl groups and contain a large variety of derivatives including simple phenols, phenylpropanoids, benzoic acid derivatives, flavonoids, stilbenes, tannins, lignans and lignins. Phenolic compounds are ubiquitous in cereals, legumes, oilseeds, nuts, herbs, fruits and vegetables, but their occurrence in animal tissues and nonplant materials is generally due to the ingestion of these plant materials (Yanishlieva-Maslarova and Heinonen, 2001). Information on the bioavailability and absorption of feed phenolics are diverse, fragmentary and controversial. Their metabolism is influenced by factors as number of constitutive carbon atoms, molecular size, lipophilicity, solubility, gastric and intestinal transit time, membrane permeability and internal pH. Plant phenolics are multifunctional and can act as reducing agents (free radicals terminators), metal chelators, and singlet oxygen 162 Panagiotis E. Simitzis and Stelios G. Deligeorgis quenchers. Examples of common plant phenolic antioxidants include flavonoid compounds, cinnamic acid derivatives, coumarins, tocopherols and polyfunctional organic acids (Pratt and Hudson, 1990). Flavonoids are a large group of polyphenolic compounds (diphenylpropanes) that occur commonly in plants. The major subgroups of them are the flavonols, the flavones, the isoflavones, the catechins, the proanthocyanidins, and the anthocyanins. Flavonoids are secondary metabolites that are endowed with biological activities and mainly act as primary antioxidants by scavenging free radicals and functioning as chain breakers (Madhavi et al., 1996). The position and degree of hydroxylation are of primary importance in determining antioxidant activity of flavonoids (Pratt and Hudson, 1990).
ANTIOXIDANTS IN ANIMAL PRODUCTION
The nutritional strategies to improve animal products quality are a relatively new approach that has emerged at the interface of animal science and human nutrition. It reflects the changing concepts of food in human nutrition, from a past emphasis on meeting nutrient requirements to an emphasis on health-related effects on foods, helping to reduce the risk of chronic diseases. Addition of antioxidants has been effectively used to alter animal products characteristics with the intention to enhance its health-related properties by minimizing residues in the product or the environment. The traditional practice of adding antioxidants during processing can still play a very important role since the added compounds have the potential for enhancing the activity of the inherent antioxidants systems (Zhang et al., 2010). Grain based feeds for farm animals are rich in n-6 polyunsaturated fatty acids (PUFAs) and results in animal products that are rich in n-6 fatty acids and poor in n-3 PUFAs. Although green grass and leaves are rich sources of n-3 PUFAs, their supplementation level in animal diets are constantly decreased. As a result the ratio of n-6 PUFAs/ n-3 PUFAs is increased with negative implications on consumer health. Supplementation of animal diets with n-3 PUFAs is becoming an accepted practice to improve the nutritional quality of lipids in animal products. However, by increasing the proportion of n-3 PUFAs in plasma, this strategy also enhances its susceptibility to lipoperoxidation. Dietary antioxidants are efficient means to limit lipoperoxidation in vivo and researchers have focused on natural molecules to satisfy consumer concerns over safety and toxicity (Gladine et al., 2007) Dietary supplementation with antioxidants appears to be a more effective way of retarding lipid peroxidation of animal products and controlling its stability compared to post mortem addition of antioxidants. Feeding an animal with a diet supplemented with antioxidants, seems to enable these substances entering the circulation system, be distributed and retained in tissues. It is well accepted that vitamin E supplementation in animal diet can improve the quality of animal products by limiting oxidative deterioration. Several studies support that vitamin E supplementation can improve meat quality and reduce lipid oxidation in poultry (O‘Neill et al., 1998), pork (Boler et al., 2009), beef (Chan et al., 1996), and lamb (Guidera et al., 1997). Moreover, compounds from herbs and spices contain many phytochemicals which are potential sources of natural antioxidants including phenolic diterpenes, flavonoids, tannins and phenolic acids. Dietary supplementation with the plant parts or the essential oil or The Effects of Natural Antioxidants Dietary Supplementation … 163 specific compounds isolated from the essential oil of numerous herbs and fruits (oregano, clove, coriander, ginger, thyme, rosemary, sage, savory, teas, grape, citrus etc) has been proved to be a simple and convenient strategy to uniformly introduce a natural antioxidant into phospholipid membranes where it may effectively inhibit the oxidative reactions at their localized sites. Available hydrogen atoms from phenol and allylic groups react with lipid and hydroxyl radicals and convert them into stable products, protecting products against the primary oxidative process. At the same time, no negative implications on products quality properties have been observed (Cuppett, 2001).
Table 1. Examples of antioxidants dietary supplementation in farm animals
Additive Major Components Concentration Reference Egg Yolk Thyme Carvacrol, Thymol 3% Botsoglou et al., 1997 Oregano essential Carvacrol, Thymol 100-200 mg/kg Florou-Paneri et al., 2005 oil Rosemary Carnosic acid, carnosol, 5-10 g/kg Florou-Paneri et al., 2006 rosmanol, rosmarinic acid Saffron Crocins, crocetins, picrocrocin, 10-20 mg/kg Botsoglou et al., 2005 safranal Citrus, Grapefruit Hesperitin, Naringenin 0.05% (of the Lien et al., 2008 components) Chicken Meat Oregano Carvacrol, Thymol 3% Young et al. 2003 Oregano essential Carvacrol, Thymol 50-100 mg/kg Botsoglou et al., 2002 oil Green Tea Catechins 300 ppm Tang et al., 2001 Rosemary and Carnosic acid, carnosol, 500 mg/kg Lopez-Bote et al., 1998 Sage rosmanol, rosmarinic acid Turkey Meat Oregano essential Carvacrol, Thymol 100 mg/kg Govaris et al., 2005 oil Rosemary Carnosic acid, carnosol, 5-10 g/kg Govaris et al., 2007 rosmanol, rosmarinic acid Rabbit Meat Oregano essential Carvacrol, Thymol 100-200 mg/kg Botsoglou et al., 2004 oil Pork Meat Rosemary Carnosic acid, carnosol, 40 ppm Haak et al., 2008 rosmanol, rosmarinic acid Oregano essential Carvacrol, Thymol 0.25-1.0 g/kg Simitzis et al., 2010 oil Sheep Meat Oregano essential Carvacrol, Thymol 1.0 g/kg Simitzis et al., 2008 oil Beef Green Tea Catechins 1.0 g/kg O‘Grady et al., 2006 Rosemary Carnosic acid, carnosol, 1.0 g/kg O‘Grady et al., 2006 rosmanol, rosmarinic acid
164 Panagiotis E. Simitzis and Stelios G. Deligeorgis
The use of natural antioxidants can extend the shelf life and increase the acceptability of meat during retail display. Nutritional approaches are often more effective than direct addition of the antioxidant to the muscle food since the compound is preferably deposited where it is most needed (Govaris et al., 2004). Dietary supplementation allows uniform incorporation of antioxidants into the subcellular membranes where it can effectively inhibit the oxidative reactions at their localized sites and improve meat quality due to delayed lipid oxidation and muscle discoloration. Increased antioxidative status in the living animal and a following increased oxidative stability of the raw product is considered beneficial for both the consumer and the processing industry (Zhang et al., 2010). Lipid composition of chicken egg has been a primary area of concern due to the relationship of specific dietary lipids with the development of diseases (coronary heart disease etc). As a result, hen feeding strategies are focused on increasing the n-3 fatty acid content of eggs. However, the content of poultry diets is increased in polyunsaturated fatty acids having negative implications on egg quality. Supplementation of poultry diets with antioxidant substances seems to be an efficient mean for improving the oxidative stability of eggs. Antioxidants interact with phospholipids, increasing the yolk‘s antioxidant potential and improving egg storability. They are effectively transferred into the egg and its concentration in the egg yolk is a reflection of the dietary supplementation (Cherian et al., 1996). High producing dairy cows are prone to oxidative stress, and the situation can be exacerbated under certain environmental, physiological, and dietary conditions. Antioxidants have important effects on the expression of genes involved in the antioxidant status, which may enhance animal health and reproduction. Moreover, antioxidants may contribute to decrease the incidence of spontaneous oxidized flavour in milk enriched in polyunsaturated fatty acids (Lindmark-Mansson and Akesson, 2000). However, more research is required to improve our knowledge on metabolism of antioxidants in dairy cows and how they can contribute in decreasing milk oxidation.
CONCLUSION
Natural antioxidants appear to be an alternative to synthetic additives in animal products industry. As it has been demonstrated, dietary antioxidants administration positively influences products quality characteristics, mainly by retarding lipid oxidation. Application of herbs, spices and essential oils with antioxidant effects comparable to synthetic additives is still remote for three major reasons: limited data about their effects in animal products, strong odor, and high cost. At the same time, the bioavailability of antioxidants could not be directly demonstrated, because adequate analytical methodology for identification and quantification of these components and their metabolites is not yet available for animal products. At the same time, large gaps exist in our knowledge of natural occurring antioxidants metabolism in animals and its changes at different physiological status. No systematic study has been conducted to examine how antioxidants are delivered to peripheral tissues and animal products and what is the fate of the components absorbed from the diet. The understanding of these mechanisms would be beneficial for commercial animal production, giving us the opportunity to adjust the optimal natural antioxidant supplementation for different animal species depending on their age, physiological status and productivity. The Effects of Natural Antioxidants Dietary Supplementation … 165
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Chapter 8
A RISK ANALYSIS OF COMPOUND FEED CONTAMINATION
Marcel van Asseldonk1, Miranda Meuwissen2 and Ruud Huirne1 1Agricultural Economics Research Institute, Wageningen University and Research Centre, Hollandseweg 1, 6706 KN Wageningen, the Netherlands 2Business Economics, Wageningen University, Hollandseweg 1, 6706 KN Wageningen, the Netherlands
ABSTRACT
By means of risk-financing instruments recall losses caused by contaminated compound feed can be pooled and transferred to other parties, i.e. the insurance industry. With knowledge of the occurrence of a contamination crisis and related damages, the probability distribution of the risk can be approximated. Reliable risk estimation is often hampered because of the lack of a complete claim distribution. Even when there would be a reliable claim data set, the relevance of such historical claims to modelling the future is dubious. The sophisticated quality assurance and tracking and tracing system which spans the entire production chain have reduced the risk of feed contamination substantially. Evidently given the often sparse data in the risk analysis of such food safety related issues it would be desirable to bring more information into the process of specifying the probability distribution. Inevitably, there must be much subjectivity in this process and there will be scope for disagreement on how best to proceed. By means of stochastic simulation the effects of elicited deviations of the ‗best guess estimates‘ for the insureds, insurers and re-insurers can be addressed. The revealed sensitivity of the outcomes enables to support the decision-making process in the design and pricing of a product recall insurance in a more transparent way.
Keywords: Recall insurance; expert elicitation; stochastic simulation.
170 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne
1. INTRODUCTION
The design and pricing of recall insurance in food supply chains is a challenging and complex task. On the one hand risk prevention receives prominent attention, therewith reducing the risk of food safety crises and related claims (Segerson 1999; Henson and Hooker 2001; Valeeva et al. 2005). On the other hand the number of recalls is increasing (Teratanavat and Hooker 2004) and traceability systems allow claims to be pushed back into the chain (Meuwissen et al. 2003). At the same time, third-party verifiability of due diligence is getting increasingly important, both for counterattacking claims and for proving the unintentional character of a (recall) loss. Also, with the 2005 implementation of the General Food Law (EC/178/2002) adequate performance with respect to traceability and recalls is no longer facultative but legally required. Due to such developments in food supply chains, insurers face difficulties in designing adequate recall insurance schemes and in calculating actuarial sound premium levels. Since food crises occur irregularly in time and place, it is difficult to derive general properties and predictive values about the probability of occurrence. Also, the probability distribution describing the possible magnitude is difficult to ascertain in a dynamic risk environment. The latter is highly depending on, among others, the source and level of contamination. As a consequence, insurers may opt for higher risk loadings or an increasing number of perils and losses excluded from the cover, therewith reducing the insurability of food-related recall risks. A reduced availability of insurance cover is generally not considered beneficial for society as a whole (Arrow 1996). Skees et al. (2001) specifically address the positive incentives of recall insurance for improving the level of food safety. Indeed, in discussing the feasibility and efficiency of risk financing instruments the aspect of incentives for risk prevention plays a crucial role. Risk prevention should be stimulated by differentiating premiums and deductibles according to measurable risk factors. Risk prevention is also encouraged by contract specifications on ‗due diligence‘. This implies that losses are only indemnified if an insured compound feed producer can demonstrate that it has taken all feasible actions and measures to prevent losses. Risk prevention is furthermore stimulated by a proper assessment of actual losses. Of the various food-related risks, this paper focuses on the Dutch compound feed industry and its inherent risk exposure. The recent large contamination crises of compound feed with dioxin (1999), MPA (2002) and dioxin (2006) in the Netherlands drew attention to the risk of product recall. This chain is characterised by a few large supplying and processing industries and many small farms. Recall risks are fairly straightforward and work well as an illustration for other food-related risks. Furthermore, the analysed case captures a classical dilemma in designing and pricing issues since reliable risk estimation is hampered because of the lack of a complete claim distribution while at the same time the need to transfer the risk is profound. Therefore the objective of this research is how to construct an analytical model quantifying the loss potential, in which the compound feed industry serves as a typical example, and to discuss the dilemmas actuaries are confronted with. Hereto feed safety regulations and past contamination crises are elaborated on. Subsequently, stochastic A Risk Analysis of Compound Feed Contamination 171 simulation modelling is outlined and the default results as well as a what-if analysis are presented. Finally, tools and their limitations for calculating premium levels in food supply chains are discussed as well as their impact on the design of viable risk-financing instruments.
2. EXISTING FEED SAFETY REGULATIONS AND QUALITY STANDARDS
Animal feeds form a major link in the animal production chain: they have a direct influence on the quality and safety of food of animal origin. Consumers of meat, milk and eggs expect the industry and the retail to supply safe, high-quality products. The government has therefore established stringent rules in the animal feed legislation which guarantee the quality of animal feeds and consequently the quality of meat, milk and eggs. Compound feed and premix companies should implement their own measures for responsible processing. The regulation provides for the control of the levels of undesirable substances and products such as heavy metals, pesticides and aflatoxin. Companies in the animal feed sector may go a step further by progressing to Good Manufacturing/Managing Practice. Thanks to this regulation, which is better known as the GMP regulation, companies can demonstrably guarantee that animal feeds and animal feed ingredients meet the legal rules and the super-legal requirements which have been agreed upon between the parties in the chain. The GMP regulation applies to producers of and traders in compound feeds, animal feeds, premixes and additives as well as transport, storage and transshipment, cultivation, storage and livestock farm. In addition, laboratories which carry out analyses as part of company checks have to be certified. The GMP regulation is voluntary and companies are therefore free to participate or not. With effect from July 2003, the certification of companies is done by certification bodies approved by the Product Board Animal Feed. A GMP certificate will only be issued to companies which meet all the quality norms. The period of validity of the certificate is three years during which interim audits will be carried out. Approximately 95% of the Dutch feed compound sector is by now GMP certified. Another example of a commitment to quality is the ―TrusQ‖ feed safety program in the Netherlands (a combination of ―Trust‖ and ―Quality‖). The aim is to use systematic screening of suppliers and raw materials to reduce significantly the risk of animal feeds being mixed with unwanted constituents. According to the members of TrusQ, which between them hold more than 60% of the Dutch compound feed market, it is a further deepening of the GMP standard.
3. PREVIOUS CRISES
Five recent and relevant crises (two originated in Belgium and three in the Netherlands) will be reviewed on the basis of the main stochastic variables, namely: 1) Number of compound feed companies affected; 2) Amount of contaminated feed produced and sold; 3) Amount of contaminated feed recalled; 4) Sector(s) confronted with contamination; 5) 172 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne
Number of primary producers affected; 6) Duration of standstill period; and 7) Number of livestock destructed. In January 1999, at a Flemish fat-melting company, mineral oil containing polychlorinated biphenyls (PCBs; most likely discarded oil from a waste recycling centre) was admixed to the fat delivered to animal-feed producers. Between 15 and 31 January, contaminated animal feed was distributed to poultry farms and to a lesser extent also to rabbit, calf, cow, and pig breeding and raising farms, mostly in Belgium. Small quantities were exported to the Netherlands, France and Germany. The contamination was detected by poultry farmers because of biological health effects such as hens laying fewer eggs, nervous system problems in chicks, and declining ratios of hatched eggs (Van Larebeke et al. 2001). Another crisis in the Dutch feed compound sector is the MPA (Medroxy Progesteron Acetate) contamination in June 2002. The contamination started in Ireland. Glucose-syrup from a pharmaceutical firm ended up at a firm in Belgium. This firm made deliveries of MPA-contaminated syrup to primary producers (pig farms) as well as compound feed producing companies. From these companies the contamination was distributed to pig and cattle farms. The duration in which contaminated feed was produced was not clear since the contamination was quite diffuse. Numerous companies were involved producing different feed products (compound feed as well as melasse). The duration in which contaminated compound feed was produced ranged from a short time up to 6 weeks. The contamination was detected by pig farmers because of fertility problems of their sows. In February 2003, the Dutch Product Board of Feed was informed concerning the contamination of bread meal with Dioxin. In total 225 ton of this contaminated raw ingredient was imported into the Netherlands via a compound feed company, which delivered it subsequently to other compound feed companies. Between end of December and end of January contaminated compound feed was produced. Farms affected by feeding contaminated compound feed comprised pig farms, broiler farms, mink farms, duck farms and beef cattle farms. In January 2006 a contamination of 25 times the maximum allowed amount of dioxins was noticed in pig fat used as ingredient in compound feed. Pigs exceeding 50 kg were destructed on 10 farms. The key characteristics of the previous small, medium and large sized contamination crises are summarized in Table I. Damages are generally not publicly available and not well specified into (components of) direct and indirect damages (Meuwissen et al. 2009). Most— and highest—damage figures were found for the 2002 MPA-crisis in compound and wet feed affecting 685 livestock farms throughout the Netherlands and causing aggregated direct and indirect damages of about Euro 100 million (Dutch Lower House, 2002). In contrast to damage data, technical parameters of animal feed crises are accessible at a much greater level of detail. However, the case descriptions illustrate the difficulty in obtaining a robust joint probability distribution or even ‗best guess estimates‘ of important stochastic variables.
A Risk Analysis of Compound Feed Contamination 173
Table 1. Key characteristics of recent small, medium and large sized contamination crises
Recent crisis (source of contamination, ingredient, year and country) Dioxin MPA Dioxin Dioxin Mineral oil Syrup Bread meal Pig fat 1999 2002 2003 2006 Belgium Netherlands Netherlands Belgium Number of compound feed 10 96B 4D 1 companies affected Amount of contaminated feed 500 343,541 3,214E - produced and sold (ton kg) Amount of contaminated feed -A 24,256 200 - recalled (ton kg) Sector(s) confronted with Pigs Pigs Pigs Pigs contamination Cattle Cattle Cattle Poultry Poultry Number of primary producers 1,821 685 237 275 affected Duration of standstill period - ≤ 6 ≤ 7 ≤ 3 (weeks) Number of primary producers - 29C 0 10 of which livestock is destructed A Unknown because of lack of information. B Unknown whether this reflects the number of compound feed producers or production locations, and possible double counts because compound feed producers could be affected via a number of sources. C In total 50,000 pigs from 29 companies were culled. D Number of production locations. E Based on estimation, mixing percentage was approximately 7%.
4. METHOD AND MATERIALS TO CONDUCT A RISK ANALYSIS
Insurers generally insure named perils and losses or, in case of ―all-risk insurance‖ there are usually a number of perils and losses specifically excluded. The prospective recall insurance policy under study covers damages as a result of ―dry‖ contaminated compound feed that is delivered to primary producers by the insured. All other product liability referring to the legal and contractual liability of compound feed manufactures to others from a defective product are excluded (for example ingredient content is not what was agreed upon). Indemnification comprises the following damages to the claimant: 1) growth disruption and downgraded quality of livestock as a result of intake of contaminated compound feed and losses associated with the standstill period; and 2) destruction and compensation in case livestock is culled. In line with standard recall insurance policies there is no reimbursement for material value in case of recall since this is regarded as an entrepreneurship risk (in the current case the value of compound feed manufactured). Transport and destruction of contaminated compound feed is also not covered since it still represents an economic value (e.g., sometimes as a bio-energy product). Further, malicious product tampering is excluded. Excluded are 174 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne losses arising along the production chain to other participants like slaughterhouses and retailers. In general, farms that are confronted with losses as a result of decreased market value of their products but did not purchase contaminated compound feed, nor are confronted with a standstill period, are not eligible for compensation.
4.1. Risk Modelling
A Monte Carlo simulation model is used to obtain insight into the distribution of the impact of a contamination crisis.. Monte Carlo simulation is considered an appropriate and very flexible method of investigating aspects that are stochastic in nature, such as contamination crises. Risks are incorporated by random sampling from a priori specified probability distributions within the model. Many random numbers are drawn which reflect the likelihood of different outcomes of each probability distribution. To establish stable probability distributions 25,000 replications (i.e. annual losses) were run. The quantitative assessment regarding the risk posed by contaminated compound feed to a claimant is conceptually presented in Figure 1.
Figure 1. Conceptual model of compound feed contamination.
In the simulation model different functional forms of probability distributions are embedded to capture the risk. In a number of scenarios parametric (Poisson) distributions are applied to capture the stochastic structure. However, in a what-if analysis also triangular A Risk Analysis of Compound Feed Contamination 175
(non-parametric) distributions are used because of its advantage that it can be relative easy elicited subjectively. Stochastic components reflected: 1) the probability about the occurrence of a contamination in the Netherlands; 2) number of firms which produced contaminated compound feed; 3) number of sectors affected; 4) type of sector(s) affected; 5) duration of contaminated compound feed production in a certain plant; and 7) the percentage of growth disruption / downgraded quality. What-if analyses provide useful insight into deviations of the ‗best guess estimates‘ of important variables in a model (Vose 2000), and are carried out with respect to the main stochastic parameters quantifying the probability and magnitude of a contamination. Six scenarios are constructed by combining three factors namely ―Distribution‖, ―Cluster‖ and ―Occurence‖. Scenarios differed with respect to the factor ―Distribution‖ in the functional form of the probability distribution applied. Three ―clusters‖ are constructed referring to variations in the main stochastic parameters: 1) number of firms which produced contaminated compound feed; 2) duration of contaminated compound feed production and standstill period; 3) duration and percentage of growth disruption/downgraded quality and other losses associated with the standstill period; and 4) probability that a contamination crisis will trigger culling. Also the impact of the relative risk of ―occurrence‖ with respect to size is investigated. The probability of occurrence is: 1) equal for large compound feed producers versus small compound feed producers; or 2) every ton produced has the same probability meaning that a twice as large compound feed company has a doubled probability of being confronted with a contamination.
4.2. Data Requirements
The lack of a complete claim distribution is hampering premium calculation. Historical data about previous contamination crises in the Netherlands are limited available and fragmented. Even when there is a reliable claim data set, the world has become such a turbulent place that the relevance of such historical claims to modelling the future is dubious. The sophisticated quality assurance and tracking and tracing system which spans the entire production chain have reduced the risk of feed/food contamination substantially. However, the risk of contamination of compound feed, triggering a recall action, still remains because of the intensified screening accuracy (which is a function of sampling procedure and sampling frequency) and fraudulent practices. The reliance on just a few observations of recent historical records entails a considerable risk of generating misleading results, perhaps seriously so. Not only few observations can give misleading results when evaluating the central part of a distribution (e.g., expected indemnified loss and thus risk premium), the approximation error is likely to be even higher when dealing with the tails of the distribution. Note that the tail of losses to be indemnified is of specific interest in designing and pricing the insurance contract. It is therefore wise to make some adjustments to these sparse data in order to make them more relevant to the uncertainty in the future insured period. This could entail more subjective revisions based on the beliefs about the future of the decision maker, i.e. (re-)insurer and insured compound feed producer, the analyst or experts. Because of insufficient data to parameterise the compound feed model subjective expert knowledge was elicited to complement the recent observations. Experts in the field of quality 176 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne assurance employed by compound feed industries were consulted by means of a questionnaire. Three-point estimates were elicited on projections about claims in order to get a more complete picture of the feed industry's inherent loss potential. Judgements are needed about lowest, highest and modal or most likely values of the triangular distribution. This simplicity makes it particular useful in cases when no sample data are available and the distribution is to be assessed wholly subjectively. Sector specific values were derived from objective sources, such as the aggregated production volumes of compound feed by sector and by size of manufacturers. Segmentation of production volumes by compound feed firm is a prerequisite if the contamination risk differs between segments. A handful of firms supply the bulk of the market, although there are more than 100 compound feed producers in the Netherlands registered. Moreover, segmentation of production volumes by sector is important to take into account since losses differ substantially between livestock species. The claim settlement after a contamination could be based on the difference between the profit during the loss event and the normal profit on a farm. With this approach also the impact of decreased market prices induced by changes in demand is taken into account appropriately. Note that claimants therefore need to have accurate accounting records of a number of subsequent years or production cycles. Losses for primary producers depend on the regulations during the standstill period. For example, whether or not milk and eggs are discarded, or these downgraded products can be utilized alternatively. In the current analysis, farm losses are based on average historic gross margins and compensation for livestock destructed is valued at the present average weight and average historic market values. Destruction costs of livestock are based on previous epidemic livestock crises (i.e., Aviaire Influenza, Food and Mouth Disease and Classical Swine Fever).
5. RESULTS
5.1. Results Expert Elicitation
The individual scores of ten experts as well as the aggregate statistics are presented in Table 2. Aggregated statistics comprise the average score for the panel as a whole as well as the averages for two clusters in order to illustrate the (dis)agreement. The clusters comprise the lowest 50% and the highest 50% subjective estimates per parameter. The most likely probability of a contamination occurring was estimated to be 20% per year. The most likely value amounted 15% per year in the cluster comprising estimations below the median, while for the other cluster this was 30% per year. There was even more disagreement concerning more extreme adverse outcomes about the probability of a contamination (the maximum probability was 25% versus 100% per year for both clusters respectively). Besides occurrence also the number of affected compound feed producers affected is a key input in the model with heterogeneous projections.
Table 2. Parameterisation of stochastic elements by means of expert judgements
A Missing data. 178 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne
The most likely number was 15 firms per contamination (with 5 and 25 firms per contamination in the cluster below and above the median respectively). More extreme adverse outcomes generated more disagreement (the maximum probability was 30 versus 125 firms per contamination for both clusters respectively). In summary, although the experts in the field of quality assurance received the statistics of recent observations prior to the consult, complementing it with subjective expert knowledge generated a diverse set of believes with regard to the extent of damages to be incurred.
5.2. Results What-If Analysis
The mean and standard deviation of the number of contaminated farms simulated with the Monte Carlo model are depicted in Table 3. The six what-if scenarios presented are denoted as follows: 1 ―Parametric_Overall_EQ‖, parametric distribution based on the overall elicited values and equal probability of occurrence; 2 ―Parametric_Low_EQ‖, parametric distribution based on the 50% lowest values and equal probability of occurrence; 3 ―Parametric_High_EQ‖, parametric distribution based on the 50% highest values and equal probability of occurrence; 4 ―Parametric_OverallDuration_EQ‖, parametric distribution based on the overall statistics and equal probability of occurrence; 5 ―NonParametric_Ov erall_EQ‖,: non-parametric distribution based on the overall statistics and equal probability of occurrence; and 6 ―Parametric_Overall_NEQ‖, parametric distribution based on the overall statistics and different probability of occurrence (i.e., every ton produced has the same probability). Scenario 4 is added to study the impact of alternative interpretations of the expert judgements. In the other scenario‘s the duration of contaminated compound feed production was based on the judgements at farm level and per sector (part of the question- nnaire focussing on duration of growth disruption/downgraded quality). The latter scenario assumed that all affected compound feed companies produced contaminated feed during the whole contamination period and were detected at the same time. Thus the median duration for the panel as a whole is 7 days in this scenario (Table 2) for all sectors.
Table 3. Number of farms affected if contamination occurs
Scenarios Number of farms affected Mean St.D. Parametric_Overall_EQ 659 760 Parametric_Low_EQ 112 200 Parametric_High_EQ 3,945 4,180 Parametric_OverallDuration_EQ 806 951 NonParametric_Overall_EQ 1,376 1,450 Parametric_Overall_NEQ 3,790 2,502
The average number of contaminated farms as well as the levels of other moments of the distribution differed substantially between the scenarios. Per crisis, on average 659 farms were affected on basis of the overall expert believes fitted by means of parametric probability distributions. The standard deviation amounted to 760 contaminated farms, which is a A Risk Analysis of Compound Feed Contamination 179 substantial dispersion in comparison to the mean. Applying triangular probability distributions instead of Poisson distributions resulted on average in 1376 affected farms per crisis. Deviations between parametric assumptions and non-parametric distributions originated from the fact that the latter assumed relative fatter tail distributions (more dense tails), while adverse outcomes were more rare given the current assumptions for the parametric approach. Thus interpreting the subjective assumptions alternatively has profound implications, but it is arbitrary which scenario understates or overstates the true risk. The distribution of the other scenarios were also based on a diverse set of believes and generated therefore also different outcomes. Only including assumptions stemming from the 50% lowest values generated on average 112 affected farms, while this was 3945 if accounting only for the 50% highest values (respectively ―Parametric_Low_EQ‖ and ―Parametric_High_EQ‖). The former can be considered as an extreme optimistic view of the true risk, while the latter as an extreme pessimistic one. If a scenario was assumed in which all affected compound feed companies produced contaminated feed during the whole contamination period and were detected at the same time the average number of affected farms was 806 (―Parametric_OverallDuration_EQ‖). Assuming that every ton produced has the same probability (―NonParametric_Overall_EQ‖) increased the average number of affected farms substantially (3790). These differences between scenarios are also present with respect to the financial consequences, quantified by means of the pure premiums. The pure premiums, also referred to as the expected claim cost or actuarially fair premium, can be derived by multiplying the loss distribution with the probability of a crisis. Note that converting the pure premium into a gross rate requires the addition of the loading, which is intended to cover transaction costs and allowance for contingencies, reinsurance and profit (Rejda 1992). In Table 4 the pure premiums are expressed in terms of amount of compound feed produced.
Table 4. Losses (i.e. pure premiums) per ton kg of produced compound feed
Scenarios Euro per ton kg per year Mean St.D. Parametric_Overall_EQ 0.09 0.32 Parametric_Low_EQ 0.01 0.06 Parametric_High_EQ 1.40 3.36 Parametric_OverallDuration_EQ 0.10 0.34 NonParametric_Overall_EQ 0.26 0.84 Parametric_Overall_NEQ 0.54 1.39
Pure premiums amounted for the pool as a whole to 0.09 Euro per ton kg per year (―Parametric_Overall_EQ‖), while 0.01 and 1.40 Euro per ton kg per year given the cluster below and above the median (respectively ―Parametric_Low_EQ‖ and ―Parametr ic_High_EQ‖). Assuming a parametric distribution, overall statistics and equal probability of occurrence (―Parametric_OverallDuration_EQ‖) resulted in a pure premium of 0.10 Euro per ton kg per year, while this is 0.26 Euro per ton kg per year if non-parametric distributions were used (―NonParametric_Overall_EQ‖). If it is assumed that every ton produced has the same probability the pure premium amounted to 0.54 Euro per ton kg per year (―Parametric_Ov 180 Marcel van Asseldonk, Miranda Meuwissen and Ruud Huirne erall_NEQ‖). The scenario analyses revealed substantial differences between the simulated pure premiums.
CONCLUSION AND DISCUSSION
The conducted quantitative analyses of contaminated compound feed showed that it is a risky prospect given the skewed loss distribution. In discussing the feasibility and efficiency of risk financing instruments the aspect of incentives for risk prevention plays a crucial role. Risk prevention should be stimulated by differentiating premiums and deductibles according to measurable risk factors. Risk prevention is also encouraged by contract specifications on ‗due diligence‘. This implies that losses are only indemnified if an insured producer can demonstrate that it has taken all feasible actions and measures to prevent losses. Risk prevention is also stimulated by a proper assessment of actual losses. Because of the lack of a complete claim distribution expert opinions of those working in this area (a combination of experience and understanding of current and future probabilities) were elicited to parameterize the probability distributions. Scenario analyses revealed that relatively small deviations of the ‗best guess estimates‘ and its interpretation (which functional form chosen) had a large impact on the outcomes. Our results imply that stakeholders and analysts need to give much more thought than seems to have been the case in the past on estimating probability functions that provide good descriptions of the risk to be faced in the planning period. While it is likely that these descriptions will continue to be partly based on historical data, there is a clear need to use other information and judgments to improve confidence in the model results, as was also discussed by Hardaker et al. (2004). Of course, because the fitted functions are based on additional subjective information complementing the sparse data, it may be no more reliable, and could be less reliable, than the original sparse data. Evidently given the often sparse data in the risk analysis of such food safety related issues it would be desirable to bring more information into the process of specifying the probability distribution. Inevitably, there must be much subjectivity in this process and there will be scope for disagreement on how best to proceed. The important point for present purposes is that the assumptions made should imply an improvement in the modeling of the future risks to be faced, not the opposite. The present model supported the decision-making process in designing and pricing insurance contracts of compound feed producers. By now, groups of Dutch compound feed producers have formed several insurance pools and reinsured part of the risk.
REFERENCES
Arrow, K.J. (1996). The theory of risk bearing: small and great risks. Journal of Risk and Uncertainty 12: 103-111. Dutch Lower House (2002). Assessment of MPA-crisis. Dutch Lower House, The Hague, The Netherlands. Hardaker, J.B. Huirne R.B.M., Anderson J.R. and Lien, G. (2004). Coping with Risk in Agriculture. Wallingford: CAB International. A Risk Analysis of Compound Feed Contamination 181
Henson, S. and Hooker, N.H. (2001). Private sector management of food safety: public regulations and the role of private controls. International Food and Agribusiness Management Review 4: 7-17. Meuwissen, M.P.M., Velthuis, A.G.J., Hogeveen, H. and Huirne, R.B.M. (2003). Traceability and certification in meat supply chains. Journal of Agribusiness 21: 167-181. Meuwissen, M.P.M., Van Andel, A.A., Van Asseldonk, M.A.P.M. and Huirne, R.B.M. (2009). Eliciting processing industry damage from feed crises. British Food Journal 111: 878-892. Rejda, G.E. (1992). Principles of Risk Management and Insurance. New York: Harper Collins Publishers Inc.. Segerson, K. (1999). Mandatory versus voluntary approaches to food safety. Agribusiness 15: 53-70. Skees, J.R., Botts, A. and Zueli, K.A. (2001). The potential for recall insurance to improve food safety. International Food and Agribusiness Management Review 4: 99-111. Teratanavat, R. and Hooker, N.H. (2004). Understanding the characteristics of US meat and poultry recalls: 1994-2002. Food Control 15: 359-367. Valeeva, N. I., Meuwissen, M.P.M., Oude Lansink, A. G. J. M. and Huirne, R.B.M. (2005). Improving food safety within the dairy chain: an application of conjoint analysis. Journal of Dairy Science 88: 1601-1612. Van Larebeke, N., Hens, L., Schepens, P., Covaci, A., Baeyens, J., Everaert, K., Bernheim, J.L., Vlietinck, R. and De Poortens, G. (2001). The Belgian PCB and Dioxin incident of January–June 1999: exposure data and potential impact on health. Environmental Health Perspectives, 109: 265-73. Vose, D. (2001). Risk Analysis. Chichester: John Wiley and Sons.
In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 183-194 © 2011 Nova Science Publishers, Inc.
Chapter 9
USE OF PROBIOTICS AS DIETARY SUPPLEMENT IN CATTLE GOATS AND PIGS
Romina Ross1, Ana Apás2, Mario E. Arena1,2 and Silvia N. González1,2 1Centro Científico Tecnológico Tucumán–Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET) 2Facultad de Bioquímica, Química y Farmacia, Universidad Nacional de Tucumán (UNT), Ayacucho 471, 4000, Tucumán, Argentina. Ayacucho 471, 4000, Tucumán, Argentina
ABSTRACT
The application of potentially beneficial microorganisms to improve host defense is a new trend to increase health benefits. We developed the first specific host probiotics for goats and pigs from a mixture of lactic acid bacteria isolated from healthy animals. In both cases, the probiotic administration enhanced the body weight and was able to modify microflora balance by reducing Enterobacteria and increasing lactic acid bacteria population. The intestinal composition of fatty acids was modified, with a diminution of saturated fatty acid and an increase of the beneficial linoleic acid. On goats, the probiotics administration was correlated with a ten time diminution of fecal putrescine (cancer and bacterial disease marker) and a decrease of 60% mutagen fecal concentration, indicating the protective effect of this treatment. These results are encouraging towards the use of probiotic mixtures as functional food for goats and pigs.
INTRODUCTION
In livestock, weaning is a critical period because animals suffer gut disorders, infections and diarrhea. Over the last decades, the management of this so-called post-weaning (PW) diarrhea syndrome has involved the preventive use of antibiotics and metals (copper and zinc) in diets 184 Romina Ross, Ana Apás, Mario E. Arena et al.
(Dibner and Richards 2005). Antimicrobial substances have been widely used to improve animal health and as growth promoter factors in poultry and piglet industries (Muhl and Liebert 2007). Subtherapeutic doses of antibiotics produce an increase of body weight (3.3 to 8.8 %) and an improvement of fed conversion (2.5 to 7 %), being not clear the mechanisms that there are involved yet. Some authors consider that the decrease diarrhea incidence by antimicrobials is due to their negative effects on pathogens (Corpet, 2000; Hays, 1981; Thomke y Elwinger,1998). Antibiotics prevent intestinal irritations and so they could enhance intestinal absortion (Anderson y col, 1999; Thomke y Elwinger,1998). Therefore these substances produce a decrease in nitrogen, phosforo and manure excretion (Roth y Kirchgessner, 1993). However, increased bacterial resistance to antimicrobials and the presence of antibiotic residues in food (Mathur and Singh 2005; Zeyner and Boldt 2006), has led the European Union to consider a full ban on in-feed antibiotics by 2006. These policy changes have prompted the feed industry to propose alternative substances to control PW disorders and to improve animal growth performance (Turner y col., 2001). On the other hand, the demand for safe and qualitative meat on the market has increased, so producers are eager to find safe alternatives and dietary strategies (Allan and Bilkei 2005). Thereby, nowadays probiotic preparations are a modern, inexpensive and natural alternative to antibiotics (Guo et al. 2006) Lactobacillus and Bifidobacterium species, which reside in the intestinal tract of most animals, are able to resist the gastrointestinal environment and to produce antimicrobial substances, so they are usually used as probiotics in livestock (Simpson et al. 2004). It has been suggested that probiotics benefit animal host by stimulating appetite (Zeyner and Boldt 2006). Probiotic bacteria could act improving both intestinal microbial population balance and digestion. Maintaining intestinal microbiota balance in animals is important to prevent diseases by controlling the overgrowth of potential pathogenic bacteria. Lactic acid bacteria (LAB) are known to release various enzymes and vitamins into intestinal lumen which exert synergistic effects on digestion and alleviation malabsorption symptoms (Fuller and Gibson 1997). Bacterial enzymatic hydrolysis may enhance the bioavailability of protein and fat and increase the production of free aminoacids and short chain fatty acids (SCFA). The absorption of SCFAs contributes to the available energy pool of the host (Fernandes et al., 1987) and may protect against pathological changes in the colonic mucosa (Leavitt et al. 1978; Leopold and Eikeler 2000; Rombeau and Kripke 1990; Rolfe 2000). There is also evidence that these bacteria could stimulate the immune system (Scharek et al. 2005). Table 1 shows some positive effects of several probiotic strains in animals. The application of potentially beneficial microorganisms to improve host defense is a relative new trend to increase health benefits. We developed the first specifics host probiotics for goats and pigs from a mixture of LAB isolated from healthy animals. The probiotic suspension for pigs (108CFU/ mL) was a mixture of two strains (1% each) Lactobacillus amylovorus and Enterococcus faecium, isolated from swine faeces and characterized for their in vitro probiotic properties (resistance to simulated gastrointestinal conditions and inhibition of intestinal swine pathogens) (Ross et al. 2008) Regarding to probiotics for goats, researchers of our laboratory selected goat faecal LAB (Lactobacillus reuteri DDL 19, Lactobacillus alimentarius DDL 48, Enterococcus faecium DDE 39 and Bifidobacterium bifidum DDBA) with antibacterial activity, specifically anti- Salmonella typhimurium and Escherichia coli O111 (Draksler et al. 2004a). Use of Probiotics as Dietary Supplement in Cattle Goats and Pigs 185
Table 1. Positive effects of probiotic bacteria in animals
Probiotic Microorganisms Effects References Bifidobacterium sp. (Abe et al. 1995) (Bohmer et al. 2006; Taras et al. Enterococcus sp. 2006; Zeyner and Boldt 2006) Growth performance increase (Taras et al. 2005; Guo et al. 2006; Bacillus sp. Davis et al. 2008) Lactobacillus sp. (Angelakis and Raoult; Capcarova et al.) Bifidobacterium sp. (Siggers et al. 2008; Bird et al. 2009) Enterococcus sp. (Ross et al. 2010; Rieznichenko et Improve intestinal microbial al. 2008) Bacillus sp. population and digestion (Babinska et al. 2005) (Ross et al. 2010; Zhang et al.; Lactobacillus sp. Fajardo Bernardez et al. 2008; Yoshida et al. 2009) (Scharek et al. 2005; Rieznichenko Enterococcus sp. et al. 2008; Szabo et al. 2009)
Stimulate immune system Bacillus sp. (Scharek et al. 2007; Schierack et al. 2009) Lactobacillus sp. (Babinska et al. 2005; Wang et al. 2009)
In our study we evaluated faecal microbiological and histological analysis, growth performance parameters, and production of mutagen compounds and their indicator (putrescine) during and after probiotic supplementation to animals. Faecal Microbiological analysis: The results obtained from faecal microbiological analyses show that daily oral administration of probiotic culture (3 ml) to pigs (Probiotic Group) decreased significantly (P≤0.05) only Enterobacteria population during the trial period (35 days) compared with Non- treated animals (Control Group). Oral administration of the probiotic mixture (108CFU/mL) to goats (Probiotic Group) was able to modify microflora balance by reducing Enterobacteria population compared with Non- treated animals (Control Group), after 55 days of treatment (Apas et al.2010). These results suggest that administration of probiotic strains to pigs and goats could act by regulating the presence of GRAM negatives bacteria. In agreement with our results, others authors showed that probiotic administration positively influenced fecal microflora by decreasing the number of Enterobacteria (Hosoi et al. 1999; de Moreno de LeBlanc et al. 2008). The population decrease of invasive pathogens such as Salmonella spp. or strains of E. coli could probably be due to lactic acid produced by LAB, which lowers the pH of the intestinal content (Parvez et al. 2006). On the other hand, it is known that Enterobacteria are potential intestinal pathogens when an imbalance of the normal intestinal microbiota occurs. The decrease of these bacterial population is desirable since E. coli has been also implicated in the production of amonium, amines and some carcinogens (Chadwick et al. 1992). 186 Romina Ross, Ana Apás, Mario E. Arena et al.
Fuller (1989) suggested that as principal modes of probiotic action, a direct antagonistic effect against specific pathogenic microbes, immunostimulation as well as the competition for substrates and places to adhere to the epithelium. As yet, there are speculations about a possible effect on secretion rate, chemical composition and stability of the mucin that can influence the ability of pathogens to adhere. These mechanisms are not mutually exclusive, and some microorganisms may exert their probiotic effect due to a single or several mechanisms (Gustafsson and Carlstedt-Duke 1984). Growth Performance Parameters: Pigs of probiotic bacteria group exhibited lower feed intake (FI) than animals of Control Group (P≤0.05). Animals of both experimental groups (the non-treated control group and the probiotic supplemented fed group) exhibited body weight (BW) values not significantly different during the trial. However pigs of both experimental groups did not show significant differences on body weight during the experimental period, the ratio between body weight and FI (efficiency) was significantly higher (P≤0.05) in animals supplemented with probiotics. These results suggest that the administration of beneficial bacteria to animals, allowed pigs to achieve the same BW than animals of the non-treated control group, in consuming a significantly less amount of commercial food. So administration of probiotic bacteria improved growth performance parameters of pigs, and this positive effect was evident throughout the trial. These results are in agreement with several authors. Fumiaki et al. (1995) investigated the effect of oral administration of bifidobacteria and LAB on newborn livestock. Their results showed that mean BW gain of pigs in the group fed probiotics was greater than control group. Pollmann et al. (1980) observed that administration of L. acidophilus was an effective way to promote BW gain and feed conversion in pigs. Estienne et al. (2005) also described that probiotic administration tended to enhance average daily gain and feed consumption in pigs. Concerning to goats, a significant increase (P≤0.05) in ruminant weight was observed after probiotic administration compared with animals of Control Group. Stimulated ruminant growth has been identified as a positive probiotic effect in calves feeding (Lesmeister et al. 2004) and could explain the improved weight gain in treated calves during pre-weaning period. These results are in agreement with the higher weight increase observed in our caprine groups when they were administered with probiotics. The mechanisms by which probiotics exert their effects are largely unknown, but may involve stimulating of non-specific immune response and enhancing the system of the immune protection of healthy animals (Vitini et al. 2000). Therefore LAB are known to release various enzymes and vitamins into the intestinal lumen. Histological Analysis: Pig intestinal samples were evaluated. Histological examination revealed that there are some differences between intestinal samples of animals of probiotic supplemented group and control group. Figure 1 shows that intestinal tissue of animals of control group exhibited a greater number of eosinophils and a widespread cellular infiltration compared with samples of probiotic supplemented feed group. The eosinophilia it is usually related with parasite infection and it is also know that this infection caused growth retardation characterized by reduced weight gains or even weight loss in animals (Chan et al. 1994). The results obtained in this work suggest that probiotic administration to animals in any way could act against parasites. There are few works reporting decreases of parasite infection in animals treated with probiotics (Bautista-Garfias et al. 1999; Bautista-Garfias et al. 2001; Draksler et al. 2004b). The antiparasitic effect may be due to the production of metabolites which could produce eggs or early larval forms damage. This antiparasite effect could Use of Probiotics as Dietary Supplement in Cattle Goats and Pigs 187 probably explain in some way the reason that animals of supplemented probiotic fed group showed better values in growth performance parameters than animals of control group in our studies.
Figure 1. Histological analysis of small intestine A (Control); B (Probiotic supplemented group) and large intestine: C (Control), D (Probiotic supplemented group). The eosinophils are surround by circles.
Meat Fatty Acid Profile: Meat is recognized as an excellent source of aminoacids, minerals and vitamins. However, there are some restrictions on it‘s ingest because of lipids in it. Fat content in foods, overall of animal origin, is largely discussed because of its association with cardiac illnesses and to changes in oxidative status in tissues (Hu et al. 2001). In the last years, CLA has received great attention for its health properties like carcinogenesis (Ha et al. 1990; Ip et al. 1991) and atherosclerosis (Lee et al. 1994; Nicolosi et al. 1997) prevention, immune modulation (Hayek et al. 1999) and body fat reduction (Park et al. 1997). This polyunsaturated fatty acid is present in ruminant fats, being the major sources of CLA for human nutrition. CLA is incorporated into the phospholipidic fraction of the cellular membranes, and its incorporation level is proportional to its content in foods. Pigs, as monogastric animals, store in tissues dietary fatty with only few modifications; so nutrition plays an important role related to obtain high quality meat. There are several researches that show modifications on meat fatty acid profile by changes on animal nutrition. There are reports that demonstrated reduction on saturated fatty acid (SFA) in animal meat by feeding them with polyunsaturated fatty acid (PUFA) enriched diet (Miller et al. 1990; Romijn et al. 1998; Averette Gatlin et al. 2002; Bee et al. 2002; Dugan et al. 2004; Lampe et al. 2006) 188 Romina Ross, Ana Apás, Mario E. Arena et al.
As probiotic strains used in this work showed the ability, in vitro, to conjugate CLA, it was evaluated if the probiotic mix administration to animals, during 35 days, could modify lipid meat profile. SFAs were predominant in both experimental groups. It was observed a significant lower concentration of SFAs (P≤0.05) in meat of probiotic supplemented fed group (Treatment Group) compared with Control Group (Table 2). This result is very important because there are several studies that showed a relationship between SFAs ingestion and cardiovascular diseases incidence (Siri-Tarino et al.; Siri-Tarino et al.; Rose and Shipley 1990; Hu et al. 2001). Meat of Treatment Group shows an increased on monounsaturated fatty acids (MUFA) and PUFA, compared with Control Group. Linoleic acid (C18:3) and CLA (C18:2) were the most abundant MUFA in meat of Probiotic supplemented fed Group and they are in a significant higher concentration (p<0.05) compared of Control Group. Our results showed that the probiotic administration in pigs could change and improved meat fatty acid profile of animals. These modifications could be related with conjugation properties of supplemented bacteria. Biogenic amines determination: It was evaluated antimutagenic effect of probiotic administration in goats. Bacterial growth of Salmonella thyphymurum TA 100 was used to determine the antimutagenic effect since only revertant cells were able to grow. Fecal samples from the Treatment Group developed a significant decrease in the number of colonies compared with the account obtained from Control Group.
Table 2. Meat Fatty Acid Profile
Fatty acid Treatment Group Control Group C14:0 116.9 ± 19.2 a 193.8 ± 17.7 b C15:0 16.9 3.1 a 16.6 3.6 a C16:0 213.3 ± 24.6 a 198.9 ± 8.1 a C16:1 12.0 ± 0.5 a 10.6 ± 1.0 a C18:0 120.9 ± 16.1 a 111.9 ± 14.7 a C18:1 (cis 9) 314.3 ± 12.9 a 298.1 ± 10.9 a C18:1 (trans 11) 23.4 ± 2.5 a 24.1 ± 6.1a C18:2 150.2 ± 14.2 a 131.7 ± 10.2 a C18:3 14.7 ± 1.5 a 5.9 ± 3.4 b cis-9,trans-11 CLA 9.8 ± 0.4 a 3.6 ± 0.8 b
The results were represented as mean ± standard deviation and they were expressed as g/100 g. SFAs 47.2 a 52.4 b Different superscript letters in the same row indicates significant differences (p < 0.05). CLA: conjugated linoleic acid. SFAs: saturated fatty acids. MUFAs: monounsaturated acids. PUFAs: a a MUFAspolyunsaturated acids. 35.2 33.4
a a PUFAs 17.6 14.2 These results suggest a protective probiotic effect that could prevent mutagen formation in the intestinal tract. Our results with the Ames test (Lankaputhra and Shah 1998) agree with the antimutagenic properties of human probiotic strains (Matsumoto and Benno 2004). The Use of Probiotics as Dietary Supplement in Cattle Goats and Pigs 189 same authors indicated a direct relation between antimutagenic effects and a decrease of a cancer marker like putrescine. This antimutagenic effect was observed in goats for the first time. Putrescine, spermine and spermidine values (μg/g feces) found in samples of Control Group, at the end of the assay, were significant (P≤0.05) higher in Control than in Treatment Group. These findings represent the first report on biogenic amines levels in goat feces with and without probiotic feed supplement. Putrescine has been correlated with diseases after weaning, suggesting that these biogenic amines are made in the small intestine of animals severely affected with diarrhea caused by pathogenic bacteria (Porter and Kenworthy 1969; Chadwick et al. 1992). They could be partially associated with Enterobacteriaceae polyamines production. In concordance with our results, it have been reported that children with malabsorption problems present high levels of fecal putrescine (Forget et al. 1997).
CONCLUSION
In the present study we have demonstrated that probiotic administration to pigs and goats could reduce Gram negative bacteria population, and produced an enhancement in growth performance parameters. Probiotic administration could reduce indirectly the incidence of parasite infection and modified meat fatty acid profile in pigs. On the other hand, administration of mix probiotic bacteria to goats could decrease intestinal mutagenicity and putrescine levels. Different effects demonstrated are related to specific beneficial properties of bacteria assayed and they are strain-dependent. In addition, the administration of probiotic supplement to animals can be considered an important and alternative way to improve the well-being of animals, decreasing health problems, preventing enteric diseases and improving performance of productive livestock. The development of probiotic products could allow producers to increase their productivity, providing safe food for consumers and developing products highly competitive in the most exigent markets.
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Chapter 10
NUTRITIONAL ASPECTS OF THEROPITHECUS GELADA: FROM WILD-FEEDING TO CAPTIVE ANIMALS
Marcus Mau1,, Jacinta Beehner2,3 and Achim Johann4 1Institute of Animal Science, University of Bonn, Bonn, Germany 2Dept. of Psychology, University of Michigan, Ann Arbor, MI 48109-1107, USA 3Dept. of Anthropology, University of Michigan, Ann Arbor, MI 48109-1107, USA 4NaturZoo Rheine, Rheine, Germany
ABSTRACT
Extant wild populations of geladas (Theropithecus gelada) are endemic to the Ethiopian plateau with two major populations in the Simien Mountains National Park and at Guassa. Around 200 geladas are kept in 20 zoos worldwide, participating in a conservation-breeding-program. Geladas are the only primate species that feeds primarily on fiber-rich grasses. Most of these grasses include phytoliths, which prevent excessive feeding due to the amount of enamel abrasion they cause. In response to this abrasion, geladas have developed co-evolutionary compensation strategies such as high molar crowns to circumvent the mechanical defences of grasses. In the Simien Mountains, geladas take 65-82% of their diet from grass, with smaller contributions from herb roots (3-21%), herb leaves (6-16%), seeds (2-5%), fruits (0-3%), and very rarely, invertebrates (0-0.1%). Here, the grassland ecosystem has been heavily degraded by humans and their livestock. There are also reports on dwindling alpine meadows getting replaced by vegetation-compositions of lower altitudes as a result of global warming. At Guassa, which is a more intact tall grass ecosystem, geladas consumed 55% grass, 29% herb leaves, 8% herb roots, 3% invertebrates, and 5% other items. Thus, while their relative
Correspondence to: Marcus Mau, University of Bonn, Institute of Animal Science, Animal Nutrition Group, Endenicher Allee 15, 53115 Bonn, Germany. E-Mail: [email protected] 196 Marcus Mau, Jacinta Beehner and Achim Johann
intake of grass parts and herbs varies substantially across sites, geladas still obtain at least half their diet from grass. Moreover, they were only recently observed to actively hunt on locusts at Guassa. Generally, geladas are able to digest more than 50% of the fiber of their daily rations. Based on this, it was hypothesized that gelada baboons are capable hindgut fermenters. Recent physiological research has demonstrated that gelada feces ferment cell wall material coming from grass. The intensity of in vitro gas production to measure microbial fermentation in geladas was similar to that of zebras and unexpectedly, not higher than in hamadryas baboons. This result implies that gelada baboons might not be dependent solely on grass. The consumption is supported by a very high expression of salivary amylase. Consistent with that, it has been observed at Simien National Park that geladas use starch-rich roots and seeds as alternative energy sources primarily during the dry seasons. As well, captive geladas tend to favor seeds over most other foods whenever offered. In conclusion, combined studies of food plants, digestive physiology, and evolutionary ecology could help to learn more about the gelada‘s unique feeding adaptation and to implement optimized dietary plans in zoological gardens worldwide.
INTRODUCTION
The gelada is the sole surviving member of the genus Theropithecus, which includes several once-successful, but now extinct species that were distributed widely across Africa, India and Southern Europe (Delson, 1993; Jablonski, 1993; Pickford, 1993; Gibert et al., 1995; Dunbar, 1998; Rook et al., 2004). Today‘s wild populations of geladas are endemic to the Ethiopian plateau with two major populations (>1,000 individuals) in the Simien Mountains National Park and at the Guassa Plateau (Dunbar, 1993a; Beehner et al., 2007; Fashing and Nguyen, 2008). Additionally, 200 geladas are kept in 20 zoos worldwide, with most of them housed in European zoos (Johann, 2009). All of these already captive-born animals participate in a breeding-program (EEP), which is coordinated by NaturZoo Rheine, Germany (Johann, 2009). Geladas are very unique primates because of their adaptation to the grazing niche (Dunbar and Bose, 1991). They are terrestrial quadrupeds and are especially adapted to ground foraging on grasses and other food items. Moreover, they possess the highest finger-thumb opposability index of any nonhuman primate (Dunbar, 1977; 1986; Krentz, 1993; Christel, 1994). Additionally, geladas are characterized by high-crowned molars to resist wear and well-developed enamel crests to optimize the mastication of grass fibers (Jolly, 1972; Eck and Jablonski, 1987). Because geladas chew as efficiently as zebras, they were coined ―primate horses‖ (Dunbar and Bose, 1991). The nutritional protein content of the grasses on which geladas mainly feed is thought to be particularly sensitive to high temperatures (Dunbar, 1993b). As a result, geladas are restricted to an altitudinal range between 1,700 m and 4,200 m, following the distribution of Afromontane grass (Dunbar, 1998).
Nutritional Aspects of Theropithecus Gelada 197
THEROPITHECUS GELADA – THE OBLIGATE PRIMATE GRAZER? OBSERVATIONS IN THE WILD
In the Simien Mountains, grasslands have been heavily degraded by humans and their livestock. Here, geladas obtain 65-82% of their diet from grass, with smaller contributions from herb roots (3-21%), herb leaves (6-16%), seeds (2-5%), fruits (0-3%), and very rarely, invertebrates (0-0.1%) (Iwamoto, 1993; Hunter, 2001). At Guassa, which is characterized by a more intact tall grass ecosystem with a diverse herb layer, geladas consume 55% grass parts, 29% herb leaves, 8% herb roots, 3% invertebrates, and 5% other items (Fashing and Nguyen, 2009). Thus, while their relative intake of grass parts and herbs varies substantially across the sites, geladas still obtain at least half of their diet from grass parts – mainly leaves and seeds, thus qualifying them as the only graminivorous primate. Despite this categorization, in 2009, geladas were observed for the first time to chase, capture and actively feed on locusts in Guassa (Fashing et al., 2010). Geladas used a wide range of locust consumption techniques. Adult males generally consumed entire locusts in one bite, after first plucking the wings. In contrast, adult females and immatures tended to bite off the head before plucking the wings and eating the remainder in one or more bites. During that time, geladas were observed to feed more intensively on locusts than on their normal diet of grass and herbs (Fashing and Nguyen, 2009). Most interestingly, feeding on locusts probably did not have any ill effects on the gelada gastrointestinal system since there were no major changes in the consistency of the feces. The geladas actively followed the locust swarms down the mountains into the valley to feed on the insects for two days (Fashing et al., 2010). After the last locusts departed from the area, the geladas returned to their feeding grounds and resumed feeding on grass and herbs again (Fashing et al., 2010). However, although geladas are considered to be entirely grazing primates (Dunbar, 1977; Iwamoto, 1979), the observations at Guassa suggest that large quantities of easily-accessible protein in locusts are able to convert geladas into insectivores. Interestingly, being insectivorous might not be accidental in geladas since feeding on flying ants and termites has also been reported (Crook and Aldrich-Blake, 1968; Iwamoto, 1993). Nonetheless, it should be considered that this temporary insectivory might render gelada baboons vulnerable to pest control in Ethiopia (Fashing et al., 2010). Pesticide spraying is common in lower and desert regions of Ethiopia (FAO, 2009). However, if treated locusts survive and climb up to the mountain grasslands, geladas might get into contact with them and by that with the insecticides. Thus, the survival of the species could be dependent on climate and ecosystem preservation combined with a more biological pest control of locust swarms (Lomer et al, 2001; Hunter, 2005).
WHAT DO LABORATORY RESULTS TELL US ABOUT THE FEEDING OF THEROPITHECUS GELADA?
While it is difficult to perform biochemical studies on geladas in the wild, cooperations with zoological gardens that house geladas are the only chance at present to get saliva or fecal 198 Marcus Mau, Jacinta Beehner and Achim Johann samples from these unique primates. With the help of zoos, it is possible to address questions as to whether geladas are obligatory grass feeders or more flexible in their diet.
The Lack of Tannin-Binding Proteins
It is known that many plants produce a variety of secondary compounds that deter herbivorous animals from feeding on them (Harborne, 1991). One such class of secondary compounds is known as polyphenolic tannins (Shimada, 2006). Tannins act as anti-feedants for many animals by producing an unpleasant, bitter taste and by reducing digestibility of foods that reach the intestines (Shimada, 2006). Only dicotyledonous plants (‗dicots‘) like trees produce tannins, while monocotyledonous plants (‗monocots‘) like grasses do not (Iason and VanWieren, 1999). It has long been hypothesized that animals relying on foods from dicots may produce salivary tannin-binding proline-rich proteins, while animals relying on monocots are unlikely to produce such proteins (Austin et al., 1989). Interestingly, a number of dicot-feeding animals, including rats (Rattus norvegicus) and macaques (Macaca fascicularis), have been found to produce proline-rich proteins (PRP) in their saliva, while grazing mammals across several different orders appear to lack classical PRP (Shimada, 2006). One obvious reason for grazers to lack tannin-binding PRP is that they simply may not need them, given that grass is essentially tannin free. On the other hand, salivary tannin- binding proteins would seem to be of potential benefit, even to grazers like geladas, in that they would enable the animals to increase their dietary breadth by switching between grasses and tannin-rich dicot foods whenever necessary. In assays of gelada saliva, there was no evidence of PRP or other tannin-binding proteins, which is consistent with the gelada‘s unique position as the only predominantly grazing primate. Nonetheless, it must be admitted that at present, it cannot be excluded that some PRP genes are silenced in captive geladas given that they are fed almost entirely with a tannin-free diet. Indeed, studies of several animals, including rats and black rhinoceros (Diceros bicornis), have demonstrated that the expression of tannin-binding proteins can be induced in response to tannin consumption (Mehansho et al., 1983; Clauss et al., 2005). However, assuming that geladas are incapable of producing PRP even when faced with more tannin-rich food items, the lack of salivary tannin-binding proteins in geladas would support a growing body of evidence for their adaptation to a primarily grass-based diet (Jolly, 1972; Dunbar, 1977; Eck and Jablonski, 1987; Dunbar and Bose, 1991; Krentz, 1993). Nevertheless, it still remains unresolved whether the loss of salivary tannin-binding capacity drove the gelada into its narrow feeding niche, or if this loss is the result of a long process of increased specialization. Furthermore, although they are grazers, geladas cannot cope with grasses at lower altitudes because without foregut fermentation, the protein content and the digestibility of low altitude grasses are too low for them to meet their nutritional requirements (Demment and van Soest, 1985). Thus, the distribution of geladas on the Ethiopian plateau is connected to the availability of easily-digestible, montane grasses. Consequently, further global warming in connection with the regression of montane grasses raises serious questions about the species‘ future prospects of geographical distribution and survival (Dunbar, 1998).
Nutritional Aspects of Theropithecus Gelada 199
The Presence of Salivary Amylase
Another salivary protein that may be closely associated with primate dietary strategies is α-amylase. Produced by the salivary glands, this enzyme catalyzes the hydrolysis of starch into monosaccharides (Jacobsen et al., 1972). Studies of salivary amylase in a variety of species suggest that its expression or activity levels may be related to the relative starch content of a species‘ diet. Animals that generally do not take in any dietary starch, such as dogs, cats, horses, cattle, sheep, or goats, do not show significant salivary α-amylase activities (Chauncey et al., 1963; Harmon, 1993). Recently, it was attempted to semi-quantify protein expression levels of salivary α- amylase for four different anthropoid primate species including geladas. Although geladas probably eat less starch than most other Cercopithecines (due to their grass-dominated diet), they exhibited relatively high levels of salivary amylase expression (Mau et al., 2010b). Such high production of salivary amylase in geladas, however, may be due to the high intake of grass seeds (Iwamoto, 1993; Fashing and Nguyen, 2009; Dunbar, 1976). Indeed, in at least one wild gelada population, preliminary evidence suggests that seeds may be preferentially chosen over grass blades when both are available (Dunbar, 1976). Likewise, captive geladas tend to favor seeds over most other foods whenever offered (A. Johann, unpublished data). Still, caretakers must be sure to limit food items rich in starch and sugar so captive geladas do not suffer from obesity or more severe health consequences (A. Johann, unpublished data). Overall, studies to date strongly suggest that the evolution of salivary α-amylase expression has been closely linked to diet in primates and other animals. In particular, salivary α-amylase expression appears to play a critical role in the dietary strategies of primates consuming starchy food items. Cercopithecine monkeys (whose cheek pouches might be connected with enhanced salivary amylase production) and humans in agricultural societies are among the only primates known to have the highest salivary amylase production, though further research on this issue is needed across a wider range of taxa before any conclusions can be drawn.
Digestive Fermentation of Grass Fiber
Although geladas are able to use different food sources in the wild, they mainly consume montane grasses containing cellulose and hemicellulose, which are not exploitable by any mammal without digestive adaptations. Therefore, microbial hindgut fermentation has been suggested to contribute significantly to the digestive process in geladas. Gelada feces are predominantly dark green in color. Fecal matter is solid with occasional regular segmentations. In a few cases, small stones of around 3 mm in diameter were present inside the compact feces obtained from captive geladas. The gelada alimentary tract was described in details by Osman Hill (1970) including the tongue and salivary glands (Figure 1). The gelada tongue was shown to have two circumvallate papillae and numerous filiform and fungiform papillae (Osman Hill, 1970; Mau et al., accepted). As such, the gelada tongue is similar to the typical tongue of higher primates namely Cercopithecoidea and Hominoidea (Kubota and Hayama, 1961). However, in this chapter, the presence and morphology of taste buds from the circumvallate papillae of geladas are presented for the first time (Figure 2). 200 Marcus Mau, Jacinta Beehner and Achim Johann
Moreover, geladas are thought to recognize bitter taste as indicated by the occurrence of α- gustducin (Mau et al., unpublished data). In contrast to the description by Osman Hill (1970), the gelada stomach presented in this chapter was not of the usual cercopithecid pattern. Although capacious, the recent sample showed a dumbbell-shaped form (Mau et al., accepted). The only similar description of that form originates from baboon (Papio spp.) stomachs, which in the empty state are ―sharply bent upon themselves at the Incisura angularis, which is located about two-thirds the distance between cardia and pylorus‖ (Osman Hill, 1970). However, the dumbbell shape of the gelada stomach may therefore be a pathological finding.
Figure 1. Morphology of the left submandibular gland of Theropithecus gelada. Bar represents 100 µm. (photo: M. Mau).
Figure 2. Taste buds from the circumvallate papillae of the gelada tongue. Bar represents 50 µm. (photo: M. Mau). Nutritional Aspects of Theropithecus Gelada 201
Figure 3. Gas production curves (ml) after incubation of feces from hamadryas baboon (x), zebra (■) and gelada (▲) with a hay in the in vitro Hohenheim fermentation gas test (HGT) over 96 h.
Morphologically, the intestine of geladas is similar to that of baboons and mostly characterized by the capacious colon (Osman Hill, 1970). Together with the observed fermentative ability of fresh gelada feces, it is considered that the very prominent colon is the most likely place of fermentation of cell wall material in geladas. However, the exact characterization of the gelada‘s fermentation pattern is still unknown. In fact, in primates, two main regions of the gastrointestinal tract underwent specializations for cell wall fermentation – the stomach (forestomach-fermenters) and the large intestine (caeco-colic fermenters; Lambert, 1998). Forestomach-fermentation is realized only in the primate taxa of colobid monkeys, having a four-chambered stomach (Bauchop and Martucci, 1968). In most other primate taxa, caeco-colic fermentation can be found using the colon and/or caecum as fermentation chamber (Lambert, 1998). In the Hohenheim in vitro fermentation gas test (HGT) gelada feces was able to ferment cell wall material coming from grass (Mau et al., 2010a). However, the intensity of in vitro gas production to measure digestive fermentation in geladas was lower than hypothesized (Figure 3). In general, the gas amounts achieved (using fecal samples from geladas compared to zebras and hamadryas baboons with hay or concentrate standard) were unexpectedly very similar. Additionally, the amount of degraded substrates after 96 h accounted for > 75% in all three species and thus was very high. However, the HGT is not a 1:1 simulation of fermentative processes in the guts of the studied species, but rather an experimental approach to the metabolic capabilities of the bacterial populations involved. It is known that baboons contain up to 2 x 1011 total bacteria per gram of feces dry matter, whereas in horses only 1.5 to 6 x 1010 total bacteria per g feces were measured (Brinkley and Mott, 1978; Koike et al., 2000). From that, it should be considered that the use of equal amounts of fecal dry matter per species might have resulted in inoculation of less microbes for the zebras compared to hamadryas baboons. Lesser bacterial mass could therefore result in poorer performance in the HGT, which would explain the lower gas production rates in zebra samples. Probably due to 202 Marcus Mau, Jacinta Beehner and Achim Johann
(1) rapid initial substrate degradation in gelada samples, (2) slow recovery of cellulolytic bacteria in zebra, and (3) privileged promotion of the cellulolytic bacteria in fecal samples derived from hamadryas baboons, all three species showed similar amounts of degraded substrate in the HGT after 96 h (Mau et al., 2010a). Future research on hindgut fermentation in geladas is required to focus on the identification of the bacterial genera involved; and such research should further use pre-digested or pure cell walls for in vitro gas production. This will also avoid biphasic fermentation due to the initial fermentation of easily-digestible compounds, which normally do not reach the colon. Moreover, it would be of great interest and value to use feces from wild-ranging geladas in comparison to captive ones to see the possible differences in the fermentation capability and/or bacterial composition connected to the predefined diet in zoos. Nonetheless, there was no support for the theory that geladas (compared to other primates) show unique cell wall fermentation activity in adaptation to the grazing niche.
Nutritional Aspects in Zoological Gardens
Although primarily fed with fresh grass or hay and grass pellets (hay cobs), captive geladas are also provided with a higher proportion of vegetables (mainly root vegetables like carrots, celery, cucumber, red beets, salad, stem cabbage, sweet pepper and different kinds of cabbage and lettuce) and seeds and commercial monkey pellets in very limited amounts as a source of vitamins and minerals (Figure 4; Mau et al., 2009). Vegetables have been cultivated to meet human needs and thus often contain soluble sugars and low fiber compared with foods chosen by free-ranging primates (Schmidt, 2002). In zoos (NaturZoo Rheine), geladas very rarely take live food i.e. living insects like locusts or ―mealworms‖. Whenever offered, the animals showed very little to no interest in these. However, there had been occasional observations on geladas digging in the soil of their outdoor-enclosures and taking particles to their mouth. These might be small invertebrates or soil-particles. Occasional observations of eating rainworms have been made. In doing so, the geladas did not show a real ―hunger‖ for the live food (A. Johann, unpublished data). The diet in most zoos includes some animal proteins, which are always contained in commercial primate pellets. These are given in a very limited amount (once a week at NaturZoo Rheine).
Zoological garden Diet Comments Rheine*, Summer: fresh grass and vegetables (carrots, no fruit Germany celery, cucumbers, red beets, salad, stem cabbage, (low sugar - high sweet pepper, lettuce) fiber diet) Winter: hay and vegetables (optional: soaked wheat grain, vitamins) *Holder of the International Stud Book for Theropithecus gelada.
Figure 4. Dietary plan of geladas kept at NaturZoo Rheine, Germany.
As a future task, it will be favorable to know more about the chemical composition of the food items chosen by free-ranging geladas to further optimize their diet in zoos. Nutritional Aspects of Theropithecus Gelada 203
CONCLUSION
Although primarily restricted to grass and herbs, the gelada diet appears to be somewhat flexible, and geladas are able to exploit supplemental food sources such as starch-rich seeds and roots or protein-rich insects. Some of these adaptations are also visible in the species‘ saliva as found in the high expression of salivary amylase (to presumably digest dietary starch). Thus, the overall feeding strategy of geladas might be even more complex than we think. Therefore, it is suggested that future studies consider the food plants in Ethiopia, the gelada‘s digestive physiology, and their evolutionary ecology. Such comprehensive studies will certainly help us understand more about the gelada‘s unique feeding niche. With knowledge about common feeding plants, their nutritional composition, and the physiological needs of geladas, it might be possible in the future to implement further optimized dietary plans in zoological gardens worldwide to support captive breeding for this species and also to point at in situ conservation needs.
ACKNOWLEDGMENTS
The work concerning gelada saliva cited in this chapter was supported by the German Research Foundation (DFG, SU 124/15-1) and is a publication of the DFG Research Unit 771 ―Function and enhanced efficiency in the mammalian dentition - phylogenetic and ontogenetic impact on the masticatory apparatus‖. We thank Dr. K. Mätz-Rensing and colleagues, German Primate Center in Göttingen, for dissecting the male gelada and for preparing the tissue samples from submandibular and parotid glands as well as tongue.
REFERENCES
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Mehansho H, Hagerman A, Clements S, Butler LG, Rogler JC, Carlson DM. 1983. Modulation of proline-rich protein biosynthesis in rat parotid glands by sorghums with high tannin levels. Proc. Natl. Acad. Sci. USA 80:3948-3952. Osman Hill WC. 1970. Primates. Comparative anatomy and taxonomy VIII Cynopithecinae: Papio, Mandrillus, Theropithecus. Edinburgh University Press, Edinburgh, UK. Pickford M. 1993. Climatic change, biogeography, and Theropithecus. In: Jablonski NG, editor. Theropithecus: the rise and fall of a primate genus. Cambridge: Cambridge U Pr. p 227-243. Rook L, Martínez-Navarro B, Howell FC. 2004. Occurrence of Theropithecus sp. in the Late Villafranchian of Southers Italy and implication for Early Pleistocene ―out of Africa‖ dispersals. J Human Evol 47:267-277. Schmidt DA. 2002. Fiber enrichment of captive primate diets. Dissertation. Columbia:University of Missouri. 101p. Shimada T. 2006. Salivary proteins as a defense against dietary tannins. J. Chem. Ecol. 32:1149-1163. In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 207-218 © 2011 Nova Science Publishers, Inc.
Chapter 11
EFFECT OF WILTING AND PLANT TYPES ON PHYSICAL AND CHEMICAL COMPOSITION OF SILAGE
U. Y. Anele1,2, A. O. Jolaosho1, O. M. Arigbede1,2, J. A. Olanite1 and O. S. Onifade1 1Department of Pasture and Range Management, College of Animal Science and Livestock 2Production, University of Agriculture, PMB 2240, Abeokuta, Nigeria Institute of Animal Science, University of Bonn, Endenicher Allee 15, 53115 Bonn, Germany
ABSTRACT
An experiment was conducted to evaluate the effects of wilting and plant types on physical and chemical composition of silage. Three forage grasses and two legumes were harvested at vegetative stage from the Fadama (wetland areas) located within the University. The grasses were Panicum maximum, Pennisetum purpureum and Cynodon nlemfuensis while the legumes were Stylosanthes hamata and Centrosema pubescens. The forage samples were chopped into pieces of 2-3cm in length. Half of the forage samples were wilted in the open for 24 hours before ensiling; the other half was ensiled as fresh materials. The experiment was arranged in an 11 x 2 factorial experimental design with 3 replicates. A total of 66 anaerobic glass jars were used for the study. Wilting x plant species interactions were observed for some (colour and odour) of the characteristics of the silages. Generally, silages from grass/legume mixtures recorded better physical parameter scores than those of monocultures. Interactions was also observed for the dry matter (DM) (P<0.001) and pH (P=0.010) of the silages. Higher (P=0.003) DM contents were observed in silages made from wilted forages. On the contrast, higher (P>0.05) crude protein contents were observed in silages made from unwilted forages. Wilting did not have any negative effect on the fibre content of the silages. The pH values were within the range of pH for good quality silage. S. hamata
Corresponding author. Tel.: +49 228739329; fax: +49 228732295. E-mail address: [email protected] (U.Y. Anele). 208 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede et al.
silage recorded the highest CP content. Ensiling grasses and legumes together produced quality silages.
Keywords: Ensiling, forage conservation, grass/legume mixture and mineral contents.
INTRODUCTION
Feed constraint is the most important impediment to improved livestock production in most of the sub Saharan African countries (SSA) (Peters, 1988; Thornton et al., 2002). This was attributed to seasonal shortages in the quantity and quality of forage from natural pastures that provide most of the feed for animals. Ruminant animal production systems in Nigeria are generally characterized by limitations posed by non-availability of year-round feed resources due to prolonged annual dry season (Nuru, 1988). Forage conservation in form of hay and silage has been found to sustain the animals on constant weight throughout the dry season (Brockman, 1990). Silage has an edge over hay, in that when properly ensiled, it can be stored over longer period with little nutrient loss (Tjandratmadja, 1989). Ensiling is one of the most efficient, cheapest and safest ways for conserving forage. The key point for best quality silage is to make a good balance between carbohydrate and protein content in the raw material. This balance can be obtained by ensiling cereal and legumes together. In this way, sufficient fermentable carbohydrates for lactic acid bacteria are provided (Koljajic et al., 1998) and simultaneously the protein content of silage is increased (Koljajic et al., 1998; Asefa and Ledin 2001; Nayigihugu et al., 2002). In addition, mixing legumes and grasses increases biomass yield, crude protein (CP) content, nutritive value of resultant silage and soil fertility (Martin et al., 1998; Assefa and Ledin, 2001; Nayigihugu et al., 2002). Wilting of grass for silage reduces the production of effluent and increases the intake of silage dry matter. An analysis of wilting data indicated that the increase in silage intake was positively correlated with the rate of wilting (Wright et al., 1999). Wilting crops for about 1 – 4 hours before ensiling reduces the moisture content to 60 – 70%. Advantages of wilting include less weight to store, reduced seepage losses, no expense of added preservation, pleasant odour and high dry matter intake by animals (Darrels and Donald 1980; Dawson et al., 1999). Considering the advantages of silage in being stable and its ability to be preserved before the on set of the dry season coupled with longer storage period when well preserved, the study on preservation of forage crops through silage is therefore importance. Most small- holder resource-poor farmers do make a habit of wilting forages by sun-drying before ensiling. Hence the study evaluated the effects of wilting and pasture types on the qualities of silage produced from grass and legume species.
Effect of Wilting and Plant Types on Physical and Chemical Composition of Silage 209
2. MATERIALS AND METHODS
2.1. Experimental Site
The experiment was conducted at the Department of Pasture and Range Management, University of Agriculture, Abeokuta, Ogun State. The site lies within the derived savannah zone of southwestern Nigeria (which is a zone of transition between the humid forest to the further south and the west moist sub-humid guinea savanna to the north) on latitude 7o58‘N, longitude 3o20‘E and 75m above sea level. Mean monthly temperature of the area ranges from 22.50 – 30.72oC. Abeokuta has a bimodal rainfall pattern that typically peaks in July and September with a break of two to three weeks in August. Temperatures are fairly uniform with daytime values of 28 to 30oC during the rainy season and 30 to 34°C during the dry season with the lowest night temperature of around 24°C during the harmattan period between December and February. Relative humidity is high during the rainy season with values between 63 and 96% as compared to dry season values of 55 to 84%. The temperature of the soil ranges from 24.5 to 31.0°C (Source: Agrometeorology Department, UNAAB).
2.2. Sample Collection
Three forage grasses and two forage legumes were harvested at vegetative stage in November, 2007, from the Fadama (wetland areas) located within the University. The grasses were between 6 – 8 weeks of age while the legumes were at pre-flowering stage. The grasses were Panicum maximum (Guinea grass), Pennisetum purpureum (Elephant grass) and Cynodon nlemfuensis (Giant star grass) while the legumes were Stylosanthes hamata (Verano stylo) and Centrosema pubescens (Centro). The forage samples were chopped into pieces of 2-3 cm lengths. Half of the forage samples were wilted in the open for 24 hours at a temperature of 23.40C and relative humidity of 71.2% before ensiling while the other half was ensiled as fresh materials.
2.3. Experimental Design
The experiment was laid out in a 2 x 11 factorial design arrangement with 3 replicates. It consisted of eleven plant types (factors) at 2 levels i.e., wilted and unwilted. The eleven plant types were P. maximum, P. purpureum, C. nlemfuensis, S. hamata, C. pubescens, P. maximum + C. pubescens, P. maximum + S. hamata, P. purpureum + C. pubescens, P. purpureum + S. hamata, C nlemfuensis + C. pubescens and C. nlemfuensis + S. hamata.
2.4. Data Collection
The chopped wilted and unwilted herbages were preserved as silages in glass jars following the method described by Filya et al. (2007). 210 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede et al.
A total of sixty-six 1.0 L anaerobic glass jars (Weck, Wher-Oftlingen, Germany), at a density of 500 g/L were used for the study. The forage samples were ensiled for a period of 8 weeks at an ambient temperature of 260C. After the period of ensiling, the glass jars were opened, examined and visually scored for the following physical properties: odour, colour, degree of mouldiness and moistness using the following subjective grades: 1 = Bad, 2 = Poor, 3 = Good, 4 = Very Good, 5 = Excellent. Six independent scorers assessed and scored the silages. Thereafter, the pH of the silages was determined.
2.5. Chemical Analyses
The samples were milled through a 1mm sieve and sub sampled for analyses. Prior to milling, samples were oven-dried at 60oC for 96 h while DM was determined by oven-drying at 100oC for 24 h. Samples were later analyzed for crude protein (CP), ether extract (EE), ash, neutral detergent fibre (NDF), acid detergent fibre (ADF) and lignin. Crude protein (ID 984.13), ash (ID 942.05), EE (ID 963.15) and the minerals were analyzed according to the standard methods of AOAC (1995). The NDF and ADF were determined according to Van Soest et al. (1991), the NDF was determined without α-amylase and sodium sulphite. Both NDF and ADF were expressed without residual ash. Lignin was determined by solubilization of cellulose with sulphuric acid on the ADF residue (Van Soest et al., 1991). Hemicellulose was calculated as NDF - ADF. Cellulose was calculated as ADF - lignin. Non-fibre carbohydrates (NFC) were calculated as:
NFC = 1000 - CP - ash - EE - NDF, with all variables expressed as g/kg DM.
2.6. Statistical Analysis
Data were subjected to analysis of variance using the GLM procedure of SAS (2009) in a 2 x 11 factorial experiment with 3 replicates. The model was:
Yijk = µ + Di + Pj + (DP)ij + εijk
where: Yijk = observation, µ = population mean, Di = drying effect (I = 1 to 2), Pj = plant species effect (J = 1 to 11), (DP)ij = interaction between drying and plant species and εijk = residual error. Means were compared by applying the probability of difference (PDIFF) option of the least squares means statement in the GLM procedure. Differences among means with P<0.05 were accepted as representing statistically significant differences. Probability values less than 0.001 are expressed as ‗P<0.001‘ rather than the actual value.
Effect of Wilting and Plant Types on Physical and Chemical Composition of Silage 211
3. RESULTS
3.1. Silage Physical Characteristics
Interaction effects of wilting and plant species on the physical characteristics of the silages were significant for the colour (P<0.001) and odour (P=0.010) as shown in Table 1. There were no differences (P>0.05) in the mouldiness and moistness scores of the silages. Silages made from unwilted herbages had higher colour score while wilted silages recorded higher scores for odour, mouldiness and moistness. Silages made from sole S. hamata had both the lowest colour (3.60; wilted) and odour scores (3.90; unwilted). Generally, silages from grass/legume mixtures had the best colour score.
Table 1. Effect of wilting and plant types on the physical properties of the silage
Colour Odour Mouldiness Moistness Wilted PM 3.60c 4.60ab 4.90 4.65 PP 4.13bc 4.76a 4.86 4.69 CN 4.13bc 4.76a 4.90 4.61 SH 3.60c 4.83a 4.86 4.67 CP 4.16b 4.86a 4.86 4.50 PM/SH 4.60ab 4.83a 4.86 4.79 PM/CP 4.66ab 4.73a 4.86 4.82 PP/SH 4.46ab 4.63ab 4.88 4.69 PP/CP 4.66ab 4.46ab 4.94 4.82 CN/SH 4.53ab 4.60ab 4.89 4.90 CN/CP 4.56ab 4.76a 4.79 4.79 Unwilted PM 4.60ab 4.83a 4.73 4.42 PP 4.83a 4.73a 4.63 4.35 CN 4.83a 4.76a 4.73 4.52 SH 4.16b 3.90b 4.46 4.31 CP 4.86a 4.96a 4.76 4.43 PM/SH 4.83a 4.76a 4.63 4.62 PM/CP 4.86a 4.76a 4.66 4.51 PP/SH 4.83a 4.63ab 4.56 4.18 PP/CP 4.86a 4.76a 4.91 4.48 CN/SH 4.73a 4.73a 4.62 4.52 CN/CP 4.83a 4.83a 4.79 4.28 SEM 0.099 0.138 0.089 0.078 P value (Main effect)1 0.001 0.046 0.229 0.006 P value (Main effect)2 0.001 0.799 0.001 0.001 P value (Interaction) 0.001 0.010 0.753 0.093 *Means in each column with different superscripts are significantly different (P<0.05) PM = P. maximum, PP = P. Pennisetum, CN = C. nlemfuensis, SH = S. hamata, CP = C. pubescens.
212 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede et al.
3.2. Chemical Composition
The results of the proximate composition and pH of the silages are shown in Table 2. Interactions between wilting and plant species were observed for only the DM and pH values of the silages. The DM contents of the silages ranged from 851 g/kg in unwilted S. hamata silage to 956 g/kg in wilted mixture of C. nlemfuensis/C. pubescens silage. Higher DM contents were observed in silages made from wilted forages. On the contrast, higher CP values were observed from silages made from unwilted forages. The pH values of the silages were all below 6, ranging from 3.3 to 5.3. Silages made from unwilted forages were slightly lower (P<0.047).
Table 2. Effect of wilting and plant types on the proximate composition and pH of the silage
DM CP EE Ash pH Wilted PM 946a 67 16 139 4.7abc PP 936a 102 12 125 5.2a CN 948a 91 18 119 4.1abcd SH 953a 192 13 64 3.7bcd CP 936a 132 15 69 4.7abc PM/SH 943a 131 15 102 4.3abcd PM/CP 946a 102 16 104 4.7abc PP/SH 949a 147 14 95 4.6abcd PP/CP 939a 117 14 98 5.1ab CN/SH 953a 143 16 92 4.0abcd CN/CP 956a 112 17 94 4.4abcd Unwilted PM 864b 81 18 123 5.3a PP 863b 122 12 113 3.3d CN 863b 110 19 107 3.8bcd SH 851b 209 13 53 4.6abcd CP 870b 149 14 57 4.4abcd PM/SH 858b 145 16 89 4.9ab PM/CP 867b 115 16 91 4.9abc PP/SH 859b 166 13 83 4.1abcd PP/CP 867b 138 14 85 3.6cd CN/SH 858b 161 16 81 4.2abcd CN/CP 862b 132 17 82 4.1abcd SEM 4.3 1.6 1.4 1.9 0.25 P value (Main 0.592 0.001 0.001 0.001 0.003 effect)1 P value (Main 0.001 0.001 0.696 0.001 0.047 effect)2 P value (Interaction) 0.003 0.157 0.996 0.978 0.001 *Means in each column with different superscripts are significantly different (P<0.05) PM = P. maximum, PP = P. Pennisetum, CN = C. nlemfuensis, SH = S. hamata, CP = C. pubescens. Effect of Wilting and Plant Types on Physical and Chemical Composition of Silage 213
Table 3. Effect of wilting and plant types on the fibre composition of the silage
NDF ADF Lignin Hemicellulose Cellulose NFC Wilted PM 564 357def 73 207 284 214 PP 627 416ab 69 211 347 134 CN 587 392abcde 84 195 308 185 SH 529 342f 74 187 267 202 CP 604 401abc 80 203 321 180 PM/SH 547 350ef 74 197 276 205 PM/CP 575 376bcdef 76 199 299 203 PP/SH 577 376bcdef 72 202 304 167 PP/CP 616 409abc 74 206 335 155 CN/SH 558 367cdef 79 191 287 191 CN/CP 596 396abcd 82 199 314 181 Unwilted PM 577 375bcdef 75 203 300 201 PP 638 420a 82 218 337 115 CN 605 377abcdef 85 227 293 159 SH 553 379abcdef 77 174 302 172 CP 616 400abc 81 216 319 164 PM/SH 566 377bcdef 76 189 301 184 PM/CP 597 387abcde 77 209 309 181 PP/SH 583 399abc 79 183 320 155 PP/CP 627 405abc 81 222 323 136 CN/SH 577 410ab 81 167 329 165 CN/CP 611 405abc 83 206 322 158 SEM 3.9 7.9 3.6 9.3 9.9 3.3 P value (Main effect)1 0.001 0.001 0.099 0.001 0.001 0.001 P value (Main effect)2 0.001 0.001 0.025 0.703 0.019 0.001 P value (Interaction) 0.486 0.016 0.791 0.132 0.093 0.499 *Means in each column with different superscripts are significantly different (P<0.05) PM = P. maximum, PP = P. Pennisetum, CN = C. nlemfuensis, SH = S. hamata, CP = C. pubescens.
Wilting x plant species interaction (P=0.016) was only observed for the ADF content of the silages (Table 3). Wilting did not have any negative effect in the over-all fibre composition of the silages. The NDF, ADF, lignin, hemicellulose and cellulose contents of the silages were higher (P>0.05) in silages made from unwilted forages. Despite their high fibre contents, silages from both wilted and unwilted forages contained significant amounts of NFC. Table 4 shows the mineral composition of the silages. There were significant (P<0.05) interactions between wilting and plant species for the phosphorus, copper and iron contents of the silages. Silages produced from wilted forages had higher amounts of these minerals than those from unwilted forages. The content of Ca ranged from 2.0 mg/kg DM in unwilted sole C. nlemfuensis and P. maximum silages to 6.33 mg/kg DM in wilted sole S. hamata silages. With the exception of C. nlemfuensis and P. maximum sole silages, all the other silages recorded Ca contents above the critical level of 3.0 mg/kg DM. 214 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede et al.
Table 4. Effect of plant type and wilting on the mineral content of the silage
Ca K P Mg Mn Cu Fe Wilted PM 2.8 3.4 3.1bc 2.1 73 4.6abcd 46abc PP 4.7 2.6 4.3a 2.0 92 5.1abc 43bcde CN 3.0 2.0 2.6bcdefg 2.2 81 2.9ef 30fghi SH 6.3 4.1 2.9bcde 1.9 68 5.4abc 56a CP 6.0 2.6 1.7g 1.6 60 6.0a 40cdef PM/SH 5.0 3.8 3.0bcd 1.9 70 5.0abc 51ab PM/CP 4.4 3.0 2.4cdefg 2.1 66 5.3abc 43bcd PP/SH 6.0 3.4 3.6ab 1.9 79 5.2abc 49abc PP/CP 5.4 2.6 3.0bcd 1.6 76 5.7ab 41bcde CN/SH 5.1 3.5 2.8bcdef 2.1 75 4.2cde 43bcde CN/CP 4.5 2.1 2.2cdefg 2.1 71 4.5bcd 36defg Unwilted PM 2.0 2.7 2.0defg 0.7 57 1.8fg 33efgh PP 3.7 2.4 2.7bcdefg 1.3 66 1.5g 23hijk CN 2.0 1.2 1.7fg 1.3 59 6.0a 17k SH 5.8 2.7 2.2cdefg 0.7 51 2.2fg 29ghij CP 6.1 2.2 1.9defg 1.2 49 3.2def 22ijk PM/SH 3.9 2.7 2.1cdefg 0.7 55 2.1fg 31fghi PM/CP 3.5 2.5 2.0defg 1.0 53 2.6fg 27ghij PP/SH 4.7 2.6 2.5cdefg 1.0 59 1.9fg 26ghijk PP/CP 4.4 2.3 2.4cdefg 1.3 57 2.4fg 23hijk CN/SH 3.9 2.0 1.9defg 1.0 55 4.1cde 23hijk CN/CP 3.5 1.7 1.9defg 1.3 54 4.6abcd 19jk SEM 0.29 0.24 0.19 0.16 2.3 0.26 1.9 P value (Main effect)1 0.001 0.001 0.001 0.194 0.001 0.001 0.001 P value (Main effect)2 0.001 0.001 0.001 0.001 0.001 0.001 0.001 P value (Interaction) 0.589 0.124 0.005 0.076 0.139 0.001 0.029 *Means in each column with different superscripts are significantly different (P<0.05) PM = P. maximum, PP = P. Pennisetum, CN = C. nlemfuensis, SH = S. hamata, CP = C. pubescens.
4. DISCUSSION
4.1. Silage Physical Characteristics
The physical characteristics (colour, odour, mouldiness and moistness) as well as pH values are indicative of silages of excellent qualities. Favourable scores for all the physical characteristics of the silages, indicated that potentially satisfactory silages were produced. Expectedly, silages from unwilted forages had better colour score than those wilted before ensiling. Scores of the physical characteristics of silages produced from the mixtures (grass/legume) were higher than those of sole grass or legume silages. This is an indication that mixtures of grasses and legumes are better preserved together. This result is in agreement with the finding of Türemis et al (1997) that scores of physical characteristics of silages were Effect of Wilting and Plant Types on Physical and Chemical Composition of Silage 215 possibly affected by different chemical composition. The greenish colour recorded for the silages (both wilted and unwilted) in this study showed they were well prepared and will be highly accepted by livestock (Akinola, 1989). On the average, mixtures of grass and legumes recorded better colour rating than sole silages of either grass or legume. The strong sweet smell perceived from the silages was an indication of good silage. McDonald et al. (1995) reported that well-preserved silage must have an acceptance aroma. Wilting of the forages before ensiling did not have any significant effect on the odour of the silages. The scores of no mould to slightly mouldy point to the good condition of the silage and that they were well preserved. Generally, silages from wilted forages were less moist than those from unwilted forages. This is in consonance with the report of Darrels and Donald (1980) that wilting reduces the moisture content of silage and hence the level of mouldiness. The pH values recorded for most of the silages were within the range of 4.0 – 4.7 for good silage (Holmes, 1989; Langer 1990; Weinberg and Ashbell, 1994; Kunkle and Chambliss, 2002). This fact suggests that activities of undesirable microorganism were slowed down by high temperature and low pH. Wilting the forages before ensiling did not affect the pH as all the silages produced were within the range stated above.
4.2. Chemical Composition
Greater DM content observed in silages from wilted forages underscores the importance of wilting forages before ensiling. A rapid drying to the target DM concentrates the water soluble carbohydrates and improves the effectiveness of the lactic acid bacteria and the quality of silage. The CP range of 67 – 209 g/kg DM recorded for the silages is well above the threshold of 60 g/kg DM required by rumen microbes to build their body protein. Below this threshold, intake of forages by ruminants and rumen microbial activity would be adversely affected (Van Soest, 1994). The CP content of the foliage fraction in this plant showed that it has the potential to contribute to ruminant feeding system as cheap protein supplement. The silage will also be adequate in providing high quality protein supplement for livestock production during the dry season. The values recorded for EE and ash contents of the silages were high enough to supply animals with their energy and mineral requirements for maintenance and other productive functions. The range of values for the fibre fractions of the silages indicated that they were diverse in terms of their cell wall contents. The range of NDF contents in our samples is below the 650 g/kg DM suggested as the limit above which intake of tropical feeds by ruminants would be limited (Van Soest., 1991). The range of NFC contents of the cowpea haulms indicated that they can be easily degraded or fermented as NFC is a crude estimate of the carbohydrate pool that differ in digestibility from NDF. It has also been reported that NFC has a positive relationship with ammonia-N utilization in the rumen (Tylutki et al., 2008). As nitrogen utilization by rumen microorganisms is related to the amount of fermentable energy, the adequate NFC contents in the cowpea haulms could enable efficient microbial protein synthesis by promoting better utilization of rumen ammonia released from feeds with high content of rumen degradable CP (Cabrita et al., 2006). 216 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede et al.
Wilting the forages before ensiling resulted in an increase in the mineral content of the silages produced. Generally, silages produced from the legumes had higher Ca contents than those from the grasses. This is in accordance to the report of Marschner (1993) that there is a marked difference between the level of Ca in legumes and grasses. The range of values recorded for Ca in the present study is higher than 0.7 – 0.9 g/kg DM reported earlier (Muhammad et al., 2005) but above the critical level of 3 g/kg DM recommended for ruminant needs in the warm wet climates (McDowell et al., 1993). The P level in this study is both above the critical level of 2.5 g/kg DM for ruminant animals and a mean value of 1.2 g/kg DM reported by Muhammad et al. (2005). Calcium and phosphorus make up to 70% of the total mineral elements in the body and have vital functions in almost all tissues in the body and must be available to livestock in proper quantities and ratio. They play special role in the proper functioning of the rumen microorganisms especially those which digest plant cellulose, utilization of energy from feeds, protein metabolism amongst other functions (McDowell et al., 1993). Calcium is considered in conjunction with P as Ca:P ratio, the recommended lower and upper critical dietary Ca:P ratios are 1:1 to 1:7 in the tropics (McDowell et al., 1993). The mean Ca:P ratio of 1:4 obtained in the present study is within the recommended range and would meet the Ca:P ratio requirement for ruminant livestock.
CONCLUSION
Silages made from the mixtures of the three grasses and C. pubescens were better in terms of colour and odour. Wilting of plant materials before ensiling lead to higher DM content but did not affect the nutrient components. The pH values were within the range of pH for good quality silage. S. hamata silage recorded the highest CP content. Ensiling grasses and legumes together produced quality silages.
REFERENCES
Akinola, J.O. (1989). Forage conservation and utilization. Paper presented at training workshop on Pasture and Range Management held at NAPRI/ABU, Zaria, Nigeria. pp. 101-105. Assefa, G., and Ledin, I. (2001). Effect of variety, soil type and fertiliser on the establishment, growth, forage yield, quality and voluntary intake by cattle of oats and vetches cultivated in pure stands and mixtures. Animal Feed Science and Technology. 92, 95-111. AOAC (1995). Association of Official Agriculture Chemists. Official Method of Analysis. 16th Edition. Washington, DC. Brockman, J.S. (1990). Grassland farming in the 1990s. Canadian Journal of Animal Science 73 (2), 3-15. Darrels, M., and Donald, M.E. (1980). Crop production, principles and practices. Fourth Edition. Macmillan Publishing Co. London, Great Britain. Effect of Wilting and Plant Types on Physical and Chemical Composition of Silage 217
Dawson, L.E.R., Ferris, C.P., Steen, R.W.J., Gordon, F.J., and Kilpatrick, D.J. (1999). The effects of wilting grass before ensiling on silage intake. Grass and Forage Science. 54(3), 237-247. Filya, I., Muck, R.E., and Contreras-Govea, F.E. (2007). Inoculant effects on alfalfa silage: fermentation products and nutritive value. Journal of Dairy Science. 90, 5108-5114. Goering, H.K., and Van Soest, P.J. (1970). Forage fibre analysis. Agric. Handbook. ARS USDA. Washington DC. Holmes, W. (1989). Grass, its production and utilization. Second Edition Blackwell scientific publication. Oxford, UK. Koljajic, V., Dordevic, N. and Grubic, G. (1998). Effects of inoculants on ensiling of maize plant and alfalfa at different ratios. Review of Research Work at the Faculty of Agriculture, Belgrade. 43(2), 95-101. Kunkle, R.E., and Chambliss. E.G. (2002). Silage harvesting, storing and feeding Florida forage handbook. Gainesvile publication. Florida, USA. Langer, R.H. (1990). Pasture, their ecology and management, Oxford University Press New York, USA. Marschner, H. (1993). Mineral Nutrition of Higher plant Second Edition. Academic press Ltd. London, Great Britain. Martin, R.C., Asatkie, T., and Cooper, J.M. (1998). The Effects of soybean intercrop biomass and protein yields. Canadian Journal of Plant Science. 78(2), 289-294. McDonald, P., Edwards, R.A., Greenhalgh, J.F.D., and Morgan, C.A. (1995). Animal Nutrition. Fifth Edition. Pearson Education Limited. Edinburgh Gate, Harlow, Essex, Great Britain. McDowell, L.R. (1985). Nutrition of Ruminant in Warm Climate. Academic Press Inc. San Diego CA, USA. McDowell, L.R., Conrad, J.H., and Humbry, F.G. (1993). Minerals for grazing ruminants in tropical regions. Bulletin: The US Agency for Literature Development and Caribbean Basin Adversary Group (CBAG) USA. Nayiguhugu, V., Kellog, D.W., Longer, D.E., and Johnson, Z.B. (2002). Case study; performance and ensiling characteristics of tall-growing soybean lines used for silage. American Registry of Professional Animal Scientist. 18 (1), 85- 91. Nuru, S. (1988). Research and development in pastoral production system in Nigeria: Past, present and future. An invited paper presented at the National Conference on Pastoralism in Nigeria held from 26 – 29th June, 1988. NAPRI, A. B. U., Zaria, Nigeria. Peters, K.J. (1988). The importance of small ruminant in rural development. Animal Resource and Development 28, 115-125. SAS®. (2009). User's guide: Statistics, Version 9.1. SAS Institute, Inc. Cary, NC, USA. Tjandratmadja, M. (1989). The microbiology and nutritive value of tropical silage Ph D. Thesis. University of Queensland, Australia. Thornton, P.K., Krushka, R.L., Henninger, N., Kristjanson, P., Reid, R.S., Atieno, A.N., and Ndegwa, T. (2002). Mapping poverty and livestock in the developing world. ILRI, Nairobi, Kenya. Türemiş, A., Kizilşimşek, M., Kizil, S., İnal, İ., and Sağlamtimur, T. (1997). Farklı katkı maddelerinin çukurova koşullarında yetiştirilen bazı yazlık yem bitkisi ve karışımlarından yapılan silajlar üzerine etkisi. Türkiye I.Silaj Kong. Bildiri Kitabı. Bursa, 5,166-175. 218 U. Y. Anele, A. O. Jolaosho, O. M. Arigbede et al.
Weinberg, Z.G. and Ashbell, G. (1994). Changes in gas composition in corn silage in bunker silos during storage and feedout. Canadian Agricultural Engineering. 36,155-156. Wright, D.A., Gordon, F.J., Steen, R.W.J., and Patterson; D.C. (1999). Factors influencing the response in intake and animal performance following wilting of grass prior to ensiling. Grass and Forage Science. 45(1), 56-63. In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 219-238 © 2011 Nova Science Publishers, Inc.
Chapter 12
FEED TO MILK: NEW TOOLS FOR THE IDENTIFICATION OF PLANT SPECIES
Silvia Gianì, Anna Paola Casazza, Luca Braglia, Floriana Gavazzi and Diego Breviario Istituto di Biologia e Biotecnologia Agraria IBBA-CNR Milano Italy
ABSTRACT
Certification of feed quality, origin and composition has become a fundamental need all over the world. The nutritional value of the feed, the traceability of its components along the agri-feed chain and the safety of the productions are intrinsically supported by such a certification. Worldwide, we are witnessing to a steep-growing request for transparency and correct information about feed and feed-derived products that comes from government institutions, producers, feed- chain distributors and consumer associations. Recently, this request has been materialized in law-enforced rules that aim to grant the traceability of the products provided with the diet to the animals, their origin and their identification across the whole chain of production, in accordance with the farm to fork concept. Even more, the composition of the feed is a compulsory request for certifying the authenticity of those high-quality traditional and regional products, that are alternatively assigned the different PDO, PDS and TSG labels. Technology should efficiently support such a request. Investigation tools and procedures should ideally be simple, rapid, reproducible, affordable, versatile, exportable from lab to lab and capable of providing an easy and immediately comprehensible output. We have identified such tools in plant introns of both nuclear and plastidial origin. They are amplified principally by EPIC-PCR, a conceptually simple approach, providing an easily identifiable genetic bar code for plant species that are found in feed, forages, pasture grasses and milk. Here we report about the success of such innovative methods and their several advantages compared to most popular yet more laborious, more demanding and expensive techniques. By combining the results we can obtain in feed and milk, the whole chain of production can be reconstituted and genetically validated.
220 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al.
INTRODUCTION
The growing quest for a high level of protection of human and animal health has been of great concern in the last decades. In the food industry, consumer protection has become an important issue to national and international institutions. As a consequence it has been one of the fundamental aims of food law, as laid down in Regulation (EC) No 178/2002 of the European Parliament and of the Council, entered into force in 2005 and since then applied to food business [1]. One of its purposes is to establish guiding principles in order to ensure a high level of health protection. Food law provides a basis for consumers to make informed choices in relation to the food they consume, safeguarding categories at risk, (i.e. people allergic or intolerant to specific food or additives) and preventing fraudulent practices and adulteration of food. Within the context of food law, it has been considered appropriate to include requirements also for feed, including its production and use where the feed is intended for food-producing animals (art 7 Regulation 178/2002 ) [1]. Documents define the roles of competent Member States authorities and describe all the categories of stakeholders in the food and feed chains indicated thereafter by the term ―food chain‖ (i.e. farmers, feed and food manufacturers, importers, brokers, distributors, public and private catering businesses). EU aims to assist all participants (or components or players) in the food chain to better understand and to apply the endorsed regulation correctly and uniformly. Particularly the art. 18 of the Regulation 178/2002, introduces the traceability requirement with the specific aim to ensure food safety and to assist in enabling unsafe food/feed to be removed from the market. It also defines ―traceability‖ as the ability to trace and follow a food, feed, food-producing animal or substances intended to be or expected to be incorporated into food or feed, through all stages of production, processing and distribution (food chain). Past food scares (BSE and dioxin crisis) have demonstrated that the identification of the origin of feed and food is very important in order to be able to identify and isolate unsafe foodstuff, guaranteeing the safeguard of the consumer. Therefore, it is necessary to establish a comprehensive system of traceability within the food and feed businesses in order to guarantee that information can be given to consumers and accurate withdrawals can be undertaken in the event of food safety problems. This is also reflected upon the request of traceability for the products provided to the animals, their origin and their identification across the whole chain of production in accordance with the ―farm to fork‖ concept. This identifies in the animal feeding a fundamental phase at the beginning of the food chain. It also refers to animal‘s identity, breed and geographical origin. This has been recently reaffirmed and enforced by EU law made active from the first of September 2010 [2]. For a long time food consumption habits were linked to local natural sources and socio- cultural factors [3]. Urbanization, people travelling, changes in transportation technologies, media influences, all have led to changes and similarity of life style and habits across regions. Nevertheless, in the last years, consumers have shown a renewed interest in food quality, more than in quantity, reclaiming clear information about their geographical origin. Therefore, traceability of the feed components is a compulsory request for certifying the authenticity of those high quality traditional and regional products that find assignment in the EU regulation as: PDO (Protected Designation of Origin), PGI (Protected Geographical Feed to Milk: New Tools for the Identification of Plant Species 221
Indication), TSG (Traditional Speciality Guaranteed) (Council Regulation EC No 510/2006) [4]. For the purpose of this Regulation: ―PDO‖ means that foodstuff are produced, processed and prepared in a given geographical area using a registered method, for examples Parmigiano Reggiano and Bitto cheeses (Italy) or Olives Noire (France).―PGI‖ means that at least one of the stages of production, processing or preparation must occur in a specific geographical area for example Borrega da Beira (fresh meat, Portugal), Coppia Ferrarese (bread, Italy). ―TSG‖ doesn‘t refer to the origin but to a traditional character either in composition or by means of production, for example mozzarella cheese (Italy) [5]. Through these trademarks EU promotes that diversity is established upon quality for the safeguard of the link between product or foodstuff characteristics and geographical origin. Particularly for PDO cheeses the entire method of production is fully ratified in the EU regulations. It is known that differences between cheeses depend upon different aspects like treatments by which milk is subjected, cheese maturing time, source of milk meant either as geographical area, animal race and feeding. Parmigiano Reggiano and Bitto are two examples of PDO cheeses that are produced in the north of Italy. Both of them have been registered as PDO products in 1996 (Regulation EC No 1263/1996) [6] and are protected with a trade mark of quality. The guidelines on their production are very strict. More in detail, for Parmigiano Reggiano (updated specification can be found in the Amendment application EC No IT-PDO- 0317-0016-26.7.2007) [7] they establish precise instructions that ratify the description of the product, aroma and taste, fat content and its characteristics like weight, dimension, appearance, thickness of the crust, colour. It establishes that the milk must come from cows reared in a well defined geographical area (territories of the provinces of Bologna, Mantova, Modena, Parma and Reggio Emilia), and that cows must be fed primarily on fodder produced within the same area, fully specified by quantity and quality. A list of prohibited fodder and by-products is also specified (i.e. silage of any kind and also fresh fodder like rice and rape). For Bitto PDO cheese, the regulation (see Amendament application Ec No IT-PDO-117- 1502-02.08.2006) [8] establishes that cheese is made of raw, whole cow‘s milk produced from traditional local brown breeds, possibly supplemented by raw goat‘s milk up to a maximum of 10% and that the area of origin of milk comprises a well defined alpine area (Valtellina and the adiacent areas like Provinces of Bergamo and Lecco) browsed only during the period from the 1st of June to the 30th of September. In this interval, herds move from the intermediate to the highest altitudes, following the richest pastures, and then move down to the former where new ground has sprouted. Details are given for the cow‘s diet, since some of the characteristics of matured cheese depend upon the original features of the milk and hence upon the feeding conditions. In specific, the cows are fed on mountain pasture grasses (in the defined geographical area) that confer to Bitto cheese a characteristic aroma and their diet may be supplemented by feeds (up to a maximum of 3 kg of dry matter per day) consisting of: maize, barley, wheat, soy and molasses up to a maximum of 3%. For this reason the Italian Consortium for the protection of Valtellina Casera and Bitto Cheese (CTCB) needs to verify, in the commercial feeds they commonly use to supplement cow‘s diet, the presence of wheat, barley, maize and soy and the lack of any other plant species. The Regulation (EC) 767/2009, entered in force on September 2010 [2] includes rules concerning the labelling of feed with the list of ingredients that must be provided in a descending order of weight calculated on the moisture content. Therefore it becomes 222 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al. fundamental to know the fodder composition in order to prevent fraud and adulteration of foodstuff. Despite this, a common and uniformly adopted method for identification of feed components is still missing. It would be therefore desirable to develop easy tools applicable to the whole food chain from animal diet to milk and milk-derived products. Here we present a couple of these tools, the TBP method for certifying plant species in feed and forages and a method that uses a chloroplast-based molecular marker (trnL) for the assessment of DNA of plant origin in milk.
TBP (TUBULIN BASED POLYMORPHISM)
Nowadays several sophisticated and accurate analytical methods are available for monitoring products that are intended for human and animal consumption. Besides their effectiveness, these methods have some limitation like the cost, that can be very high, or the need for experienced operators, the low operating speed, the low-reproducibility of the results. They are often laborious, not-flexible and cannot be applied to mixtures of ingredients as is the case for feed or traces of plant DNA in milk. In order to satisfy the demand of the CTCB consortium for a better control on feed suppliers, the potentiality of the TBP (Tubulin- Based Polymorphism) method for the analysis of compound feeding stuffs was tested [9]. The TBP method is a PCR molecular marker-based tool, that relies on the presence of intron-specific DNA polymorphism occurring within the members of the plant -tubulin gene family [10]. The -tubulin polypeptide assembles with the -tubulin moiety to make a dimeric protein that is the major constituent of microtubules, fundamental intracellular structures involved in mechanisms essential for eukaryotic cell viability, hence for the growth and development of any plant species. Tubulin‘s key role in cell biology and division has superimposed, in the course of evolution, a rather strict maintenance, across Eukaryotes, of the primary aminoacid sequence of both the - and the -polypeptides [11, 12, 13]. Each plant species contains a small number of-tubulin genes, defined as isotypes, that encode for slightly different proteins. The number of tubulin isotypes may vary among plant species. The -tubulins of Arabidopsis, rice and poplar are among the best characterized gene families with nine, eight and 20 members respectively [14, 15]. They all share a common genomic structure that provides the basis for the TBP technique, designed upon three simple facts. The first is that almost all plant -tubulin genes contain two introns of variable lengths at fixed position (first intron at position +396 nucleotides from the A of the ATG translation initiation codon and the second at position +672). The only reported exceptions are ZeamaTUB1 and OryzaTUB2 genes that contain only the first intron [15]. The second is that the length of the intron can vary between the different isotypes that define the -tubulin gene family. The third is that the introns are flanked on both sides by coding nucleotide sequences that are fairly well conserved allowing the design of universal primers suitable for EPIC-PCR amplification (Exon-Primed Intron-Crossing Polymerase Chain Reaction) (Fig. 1). Therefore introns of plant -tubulin genes become the source for DNA polymorphism in what may be defined as a new molecular marker for genetic diversity [16]. Initially, a first version of the TBP method, based on the use of the first intron as the sole source for variability, was successfully applied for assessing intra and interspecific relationships and Feed to Milk: New Tools for the Identification of Plant Species 223 differences in oilseed rape, coffee and lotus accessions [10]. Then, a second approach (TBP2) based on the additional contribution of the second intron was developed and used for the genomic profiling of the genera Eleusine, Arachis and Phaseolus [17, 18]. The combinatorial yet separated use of the two introns (cTBP) as a source for DNA polymorphism increases the number of molecular markers allowing for a more reliable phylogenetic analysis. Information from both introns can also be simultaneously achieved by amplifying, in a single reaction, the whole region that encompasses the two introns. This method, named ―horse TBP‖ (hTBP) can become helpful to confirm the results obtained with the analysis of intron I and/or II, when a higher resolution of the amplified bands is required, or in case of molecular cloning of individual intron bands [9, 19].
Figure 1. Scheme of the TBP methods. A: The scheme represents a generic plant -tubulin gene where the grey boxes define the exons and the lines, whose variable length is indicated by the double headed arrows, the introns. Each gene of a plant -tubulin family represents an isotype that is identifiable by the different length of the intron. fex1 and rex1 define the position of the forward and reverse primers relative to the amplification of the first intron (TBP); fin2 and rin2 are designed for the amplification of the second intron (TBP2). B: Schematic representation of the different TBP methods based on the alternative use of different primer combinations. 224 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al.
Overall, the TBP-based methods provide the possibility of amplifying the -tubulin introns from any given plant species, regardless of the availability of any information on the plant genome that is under investigation. TBP-based methods (European registered patent n.1144691) are rapid, simple, and reliable. The method substantially meets the requirements established by the CBOL (Consortium for the Barcode of Life) for a DNA barcoding marker capable of identifying species.They are: (i) the use of a single couple of primers; (ii) bidirectional sequencing of the amplified fragments; (iii) the upmost capacity for discrimination at species level. In addition, the TBP methods, differently from any other marker, can be applied to mixtures made by a discrete number of different plant species, as it is often the case for commercial feed. This makes the TBP method unique in its diagnostic potentiality on feed and very useful to provide certification for specific products such as the Parmesan rather than the Bitto cheese.
BIOMARKERS IN MILK
Milk is an important food due to its nutritional value and its preferential use by vulnerable subjects such as babies, children, elderly people. In the past, many studies have been conducted in order to characterize and classify the different milk components, most typically proteins and lipids. Lately, the presence of plant DNA traces possibly resulting from the diet has also been investigated because of some concern regarding animal consumption of GMO products [20, 21]. Moreover, components of the animal diet can indeed contribute to milk aroma and nutritional properties. Therefore it would be important to develop analytical tools that can unambiguously identify in milk the components of the diet provided to the animals. Analytical methods, like mass spectrometry, spectroscopic and separation techniques, have been so far used to detect and quantify compounds that are not produced by animals and whose presence can be unambiguously assigned to the animal diet. These may be carotenoid, terpens, phenolic compounds and others. These compounds have been proposed as potential biomarkers for the plant-animal-milk-cheese chain, that can be useful for monitoring cheese production including some Italian PDO products (i.e Bitto cheese) [22-25]. Some of these markers, terpens for example, are a group of plant-specific compounds particularly abundant in dicotiledons, which provide special aromatic properties that modify the flavour of the diary products [26, 27]. Although useful, these biochemical markers suffer the limit of variability that may reflect seasonal changes in the botanical composition of the meadows and additional factors linked to the plants developmental stage (for example flowers and fruits are richer in terpens compared to vegetative parts), the altitude of the pasture or the grazing management of the cows [28, 29]. Other sources of variability can be related to the animal genetic components and then to their individual ability to accumulate potential tracers. For all these reasons the analytical tools based on biochemistry seem valuable as chemical fingerprint for cow feeding characterization but not appropriate as reliable and unique analytical tools for tracing PDO cheeses or milk (like Bitto, Parmigiano Reggiano etc). Additionally, the cost and easiness of implementation vary among methods and often results are obtained with the combined use of different tracers giving rise to a large amount of data, which have to be analysed, carefully interpreted and cross-validated with chemometric tools. Feed to Milk: New Tools for the Identification of Plant Species 225
On the contrary, diagnostic tools based on DNA detection are not influenced by external factors and may be very useful as a complementary assay, at the very least. Although simple, reliable, rapid and cost-effective, methods on DNA detection are still not in use in the chain of milk production and derivatives. PCR is a molecular technique that allows the detection of very low amount of nucleic acid fragments and can therefore be used to investigate whether plant DNA fragments assumed with the diet are detectable in animal tissues or in their products like milk. To this regard, it has been already reported [30-33] that feed derived DNA can cross the animal intestinal wall and can enter the blood system. The presence of small fragments from highly abundant plant DNA (that is chloroplast DNA since it is present in a high number of copies) was originally reported in extracts from bovine lymphocytes, in all chicken organs but not in eggs and barely detectable in milk [34, 35]. Since these pioneering reports, the presence of DNA of plant origin in milk has been further investigated. Initial conflicting data [35-38], have been successively amended by the work of Phipps and Nemeth [39, 40] who definitively showed the presence of chloroplast DNA sequences in several raw cow milk samples. In our laboratory we confirmed those data showing that it is possible to identify feed derived DNA fragments in milk by either DNA sequencing or different PCR-based assays [41, 42]. These latter were based on the alternative use of trnL (leucine (UAA) tRNA), matK (maturase K) and rbcL (ribulose bisphosphate carboxylase Large subunit) as effective chloroplast-based molecular markers.
RESULTS AND DISCUSSION
TBP Method
Figure 1 reports a schematic representation of the TBP method. Each of the plant genes that encode for -tubulin is characterized by the presence of three coding exons interrupted by two introns located at fixed positions. Amplification of the introns is obtained by a PCR reaction with the use of a mixture of degenerated primers homologous to a portion of the exons that is at the boundaries of the introns. Pair of primers designed on the boundaries of the first intron defines the TBP method. TBP2 is when the pair of primers is designed on the exons surrounding the second intron. These two approaches allow the combinatorial yet separated amplification of the first and the second intron. The analysis of the results obtained applying TBP and TBP2 is defined combinatorial TBP (cTBP). In addition, the amplification of the whole -tubulin genomic region that encompasses the two introns is obtained with alternative combinations of the forward and the reverse degenerated primers designed on the first and the second intron respectively. This defines the hTBP version of the method. hTBP can sometime provide a better resolution of the DNA polymorphisms since the amplified bands are higher in sizes, up to 2 kb. In all events, the amplified products (-tubulin introns and small portions of the flanking exons) are then separated by electrophoresis on polyacrylamide gels and visualized by silver staining. Figure 2 shows one of these results. It refers to the TBP pattern obtained from the amplification of the first intron of the -tubulin genes of several plant species that can represent common source for diary animals feed. Plant species were harvested from idle fields and grasslands. As shown, each species can be easily 226 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al. distinguished from all the others by its own specific pattern that is unique and reproducible, resolved in several polymorphic bands. It is therefore possible to identify at least one band, or a combination of bands, that can be used as diagnostic element (molecular marker) for any given plant species. With regard to Fig. 2, it can be noticed that within the same taxonomic genus (Trifolium) the species T. repens, T. hybridum and T. pratense (lane 2, 3, 4) show a highly different pattern of amplification as well as the two subspecies recognized in the Arrenatherum genus (A. eliatus ssp. elatius lane 5 versus A.elatius ssp. bulbosum, lane 8). Fig. 2 also shows one of the major feature of the TBP method that is the ability of genotyping plant species in the absence of any available genome information. The method allows for a rapid and reliable comparison of the endemic herbaceous populations that characterize different fields and pasturelands. It could then be used to combine a given pasture area with its own specific panel of grasses, that may associate with differences in the nutritional value rather than the geographical origin of derived food products.
Figure 2. TBP amplification profile (Ist intron) of 12 herbaceous plants collected from idle fields and grasslands of farms located in Lombardy plain. Mk : molecular sizes marker. 1: Poa annua; 2: Trifolium repens; 3: Trifolium hybridum; 4: Trifolium pretense; 5: Arrenatherum elatius ssp. elatius; 6: Holcus lanatus; 7: Phalaris arundinacea; 8: Arrenatherum elatius. ssp. bulbosum; 9: Dactilis glomerata; 10: Poa trivialis; 11: Agrostis tenuis; 12: Lolium multiflorum. Feed to Milk: New Tools for the Identification of Plant Species 227
The specificity of the pattern, together with its unique versatility, makes the TBP method a powerful tool for the identification of individual species in mixtures. Such an example is provided in Fig. 3A where the TBP pattern of four different commercial feeds (D, F, H and L), used to supplement the diet of cows whose milk is used for making the Bitto PDO cheese, is shown in comparison with the TBP pattern of wheat, maize, barley and soy, the four allowed feed ingredients, according to the disciplinary. Feed samples were handed to us by the CTCB consortium. The four single plant species are characterized by a unique banding profile where, together with some overlapping bands, one or more band specific for either maize, wheat, barley or soy can be easily identified. Some bands or group of bands that are representative of the single plant species are marked by a circle in the Figure 3A where the TBP pattern of the D, F, H and L commercial feeds is also shown. In feed L the presence of maize and soy is clearly detectable. Feed D and H unambiguously contain wheat, barley and maize but not visible bands can be attributable to soy, even if this species is declared in the label. Soy is also absent in feed F that is characterized by a TBP amplification pattern that indicates the presence of maize, barley and wheat although this latter was not declared. The correspondence between the declared components reported in the label and those found with the TBP method is summarized in Chart 1.
Figure 3. (A) TBP-1st intron banding pattern of four commercial feeds (D, F, H, L) used for Bitto cheese production and of the four reference single species (W, wheat; B, barley; M, maize; S, soy). The declared composition of the feeds is indicated in Chart 1. Diagnostic bands are encircled. Mk : molecular sizes marker. (B) TBP-1st intron analysis on WMB mixtures containing different percentages (1, 5, 10 and 25% expressed in dry weight) of soybean meal. The arrows indicate the most representative soy‘s specific markers . W: wheat, M: maize, B: barley, S: soybean are the single species used as references; bp marker is 1 kb molecular marker.
These data and others we have collected (not shown) indicate that the detection of soy goes often neglected, although its presence is declared on the label. This may depend from 228 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al. different reasons: (i) an incorrect labelling; (ii) presence of trace amounts; (iii) different levels of degradation resulting from various treatments (mechanical stress, high temperature, high pressure, chemical extraction) of the feed. All these factors could cause a low or null DNA amplification. In order to verify such possibilities the TBP Ist intron method has been applied to differentially treated soybean samples consisting of pelleted soybean hulls, soybean meal or heat-treated full fat soybean. None of these treatments resulted in significant differences in the TBP pattern which remains that typically reported for soy (data not shown). Then, the hypothesis remains that untraceability of soy could depend on very low amounts in the mixture. To this purpose we have determined the soy detection limit of the TBP method (Fig. 3B). Increasing amount of a soybean meal preparation was added to a blend of wheat, maize and barley and the TBP Ist intron analysis was carried out. Under these experimental conditions we found a detection level that ranged between 1% to 5% w/w. Therefore, we conclude that the amount of soybean present in samples D, H and F is below the 1% threshold percentage, if not absent.
MONITORING THE FEED TO MILK CHAIN
The TBP-based methods (TBP, TBP2 and hTBP) can easily verify the composition of feeds and forages that are provided to the animals. They can detect the presence of 4 to 5 different plant species present in a given mixture. A higher number of species may in fact prevent a reliable identification since the number of TBP-amplified bands may become too high leading to a high degree of overlapping and the resolution of individual bands may become too low. This said, it remains that TBP is, under many practical circumstances, a very convenient method to provide a fast and complete survey of the components of the animal diet. This is a first level of necessary information if one wants to achieve complete traceability along the feed to milk chain. In fact, knowing the components of the animals diet is of help for trying to rescue traces of them in their products, i.e. milk. Due to the fact that in milk plant DNA is present in a very low amount and that earlier studies reported that no DNA fragments from single copy genes, including transgenes were detectable [38, 39, 43, 44], nuclear markers like tubulin genes are not considered good candidates for traceability of feed-derived plant DNA in milk. In fact, we confirm this assumption since we failed to detect any plant TBP-derivable band from total DNA extracted from raw cow or goat milk samples (data not shown). An interesting control of these experiments was the detection, in the same DNA samples extracted from raw milk, of tubulin bands of animal origin, identified with a prototype of a new version of the TBP method made feasible for animals (TBP-mediated amplification of animal targeted sequences in milk). This is not surprising since a large amount of cells are often released from the mammary glands into the milk.
Feed to Milk: New Tools for the Identification of Plant Species 229
Figure 4. PCR-mediated detection of a 297 bp DNA fragment amplified with the matKtrit1- fw/matKtrit1-rw set of primers from total DNA extracted from milk samples used for Bitto PDO cheese production (lane 1 and 2) and from two different brand-labelled milk samples sold in the Italian market (lane 3 and 4). Co + : diet positive control, Co- : control without DNA; Mk: molecular marker.
With regard to nuclear-based markers, many laboratories, including ours, reported the detection in milk of small chloroplast DNA fragments [34-41]. In accordance, we have successfully used chloroplast-based molecular markers like rubisco (ribulose bisphosphate carboxylase; rbcL), maturase K ( matK) and leucine (UAA) tRNA (trnL) genes for the identification, in raw goat and cow milk samples, of specific plant species that could be associated to the diet provided to the animals [42]. By the use of these chloroplast-based markers we demonstrated the presence of plant DNA in milk from goats that were provided with a specific monophytic diet and from raw cow milk samples that were either fetched from different stock farms or sold in the Italian market. This could be done analyzing DNA extracted from total milk or from the skimmed and the cream fractions [42]. Following up this line of research, Figure 4 reports the detection of a 297 bp single fragment obtained from the specific amplification of the chloroplast gene matK that encodes for the maturase of higher plant plastids [45]. matK specific fragments were PCR-amplified from total DNA extracted either from milk samples used for Bitto PDO cheese production (lane 1 and 2) or from two individual brand-labelled (Baronchelli) milk samples sold in the Italian market (lane 3 and 4). At the beginning, detection of matK sequences was not always successful across the dozens of milk samples we analyzed, fetched from stock farms located in the Lombardy plains and provided to us by ARAL, the regional farmer association. The matK products amplified from the two Baronchelli milk samples shown in Fig. 4, were also sequenced on both strands and their sequences were compared with the matK sequences, available in Genebank® database (NCBI), of plant species known to be commonly present in commercial fodder such as barley, mais, rye and wheat. matk DNA sequences retrieved from the two Baronchelli milk samples matched 100% with the nucleotide sequence of wheat while few mismatches could be found when the amplified matK sequence was aligned with that of other plant species (not shown). These findings revealed that the matK trit1 set of primers was biased on wheat. This specificity could cause an 230 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al. unsuccessful detection of other plant species due to the occurrence of some nucleotide mismatches in the target sequence, as it was the case for maize and soybean. This is likely to explain the negative results we obtained since they referred to milk samples produced from cow that were fed with minimal or null amount of wheat.
Figure 5. (A) PCR- mediated detection of a 220 bp rubisco DNA fragment (rbcL) amplified from total DNA extracted from 12 different plant species with the rbcLfw 120/rbcLrw 300 set of primers. 1: ryegrass; 2: rice; 3: alfa alfa; 4: clover; 5: rape; 6: meadow fescue; 7: bearded fescue; 8: poa ; 9: soy; 10: wheat; 11: barley; 12: maize. Co-: no DNA; M: molecular marker. (B): PCR-mediated amplification of a 220 bp rubisco fragment in four different milk samples collected in different stock farms located in the north Italy plain (lane 1 to 4); Co +: positive diet control (wheat); Co-: no DNA; M: molecular marker.
To further validate this hypothesis, a new set of primers was designed (rbcLfw 120/rbcLrw 300) on a different chloroplastic single copy gene, rbcL. Primers were designed to match a very well conserved rbcL region spanning from 120 to 300 bp from the ATG translational initiation codon. We selected the target sequences from multiple sequences alignment of 10 different plant species (wheat, barley, ryegrass, meadow fescue, rice, maize, alfa-alfa, clover, soy, rape). Eventually, we designed a couple of fully conservative primers with just one base degeneration in the reverse primer. This newly designed rbcL primer combination was first tested on total DNA extracted from different plant species (Fig. 5A). A single PCR-amplified product of the expected sizes (220 bp) was found in all plant species with the exception of bearded fescue (lane 6) where an additional, likely aspecific band, was also amplified. This rbcL-specific combination of primers was then used to assay for the presence of the same fragment in total DNA extracted from some of those milk samples Feed to Milk: New Tools for the Identification of Plant Species 231 where detection with matK was problematic or unsuccessful. Fig. 5B shows that the 220 bp long rbcL fragment was amplified in all the samples, confirming that detection with the previously reported matK primer combination failed because of its undesirable bias on wheat.
SINGLE PLANT SPECIES IDENTIFICATION IN COMMERCIAL FEED AND MILK SAMPLES BASED ON INTRON LENGTH POLYMORPHISM (ILP) OF THE trnL GENE.
The leucine (UAA) tRNA (trnL) gene contains an intron that can vary in length depending on the different plant species. It was already shown that the intron region of trnL could be successfully used for evolutionary studies [46] and for developing genetic markers [47, 48]. Therefore, variation in sizes of the PCR amplified trnL fragments could represents a handy devise for testing plant species distinctiveness in milk samples. To pursue this task, a primers couple (trnLfw3/trnLrw2) was designed in the conserved region of the trnL gene, flanking the intron at both the 5‘ and 3‘ ends. The occurrence of intron length polymorphism (ILP) could then be easily assayed and referred to the presence of a given plant species. As shown in Figure 6A the trnL set of primers allows the amplification of the same non coding region, yet variable in sizes, from total DNA extracted from the four plant species (wheat, barley, soy and maize) that are supplied to cows whose milk is used for the Bitto‘s cheese production. As shown in the figure the four plant species can be easily recognized by the different length of their trnL intron. Amplicon lengths are 255 bp in maize, 307 bp in soy, 369 bp in barley and 384 bp in wheat. PCR reactions were performed using two different annealing temperatures (58°C and 66°C) to meet the optimal conditions that, depending on the specific sequence of the primers, allow the detection of the four amplification fragments when more than one species must be detected at the same time, as it is the case for fodder, commercial mixtures or milk samples. In fact, the reverse primer that contains three degenerated nucleotides, has different annealing temperatures for soy (60° C) with respect to wheat and barley (68°C). An ILP pattern compatible with that of the single plant species, was obtained amplifying the trnL intron from total DNA extracted from a mixture made by adding equal amount of wheat, barley, maize and soy (left lane of Fig. 6B). As shown, the amplification pattern obtained from the mixture is consistent with the presence of all four polymorphic fragments and the efficiency by which each species-specific fragment is amplified can be influenced by the annealing temperature of the primers. Different conditions were tested to eventually find that soy specific fragments are preferentially amplified at an annealing temperature of 60 °C (left lane of Fig. 6B). This condition was therefore chosen for feed analysis, since soy is often present in very low amount and is by far the most difficult component to be detected in the commercial mixtures. 232 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al.
Figure 6. (A) PCR-mediated amplification of the trnL intron performed with the trnLfw3/trnLrw2 set of primers on 20 ng of total DNA extracted from wheat (W), barley (B), soy (S) and maize (M). 58 and 66 refer to the different annealing temperatures used in the PCR reactions; Co- : control without DNA; Mk: molecular marker. Arrows point to the different sizes of the following species-specific amplified fragments M: 255 bp; S: 307 bp; B: 369 bp and W: 384 bp. (B): Agarose gel electrophoresis of PCR products amplified with the trnLfw3/trnLrw2 set of primers specific for the intron of the trnL gene. Fragments were amplified from 20 ng of total DNA extracted from a mixture containing equal amount (25% each) of grounded plant material (WMBS), 20 ng of three commercial feeds (D, F, H) commonly supplied as fodder to cows whose milk is used for Bitto‘s PDO cheese production and 20 ng of one commercial feed from a small stock farm located in south of Italy (P). The declared composition of the commercial feeds are indicated in Chart 1 Mk: molecular marker.
Then we proceeded to analyze three commercial feeds (D, F, H, the same as in Figure 3) that are supplied in the diet of cows whose milk serves to produce the italian PDO Bitto cheese. An additional commercial feed commonly used by a small stock farm located in south of Italy (P) was also added. The PCR- trnL specific fragments amplified from the four feed samples comigrate with the corresponding fragments of the individual species run on the left lane of the gel (Fig. 6B). This provides a clear evidence that plant specific detection is feasible and consistent. Comparison of these data with those obtained with the TBP method (Fig.3) shows also a clear consistency. In fact, in feed D fragments of 384, 369 and 255 bp corresponding to wheat, barley and maize respectively could be amplified whereas no band attributable to soy can be detected. Also the results obtained on feeds F and H are consistent with those reported with TBP. Both F and H composition is different from what declared on the label. They do not contain soy, at least at a detectable amount. They rather show a high amplification signal corresponding to wheat, a plant species that is not declared in feed F. The 369 bp long fragment specific to barley was barely detectable in both the F and H feeds as opposed to what observed with the TBP method (Fig 3). To this regard, it must be noted that the length of the trnL intron fragment is very similar in wheat and barley and this may hinder detection when one of the two plant species is present in abundance. The DNA polymorphic patterns obtained from TBP or from the use of the trnL markers are consistent to each other not only for plant species identification but also for what concerns the relative amount of the single components. For example, in Fig 6 the maize amplicon is detected with higher intensity in sample D compared to F and H. The same quantitative difference is observable when TBP is used as diagnostic tool for these three feed samples (Fig. 3). Feed to Milk: New Tools for the Identification of Plant Species 233
The presence and composition of plant DNA were finally assessed in milk samples F and H, targeted to Bitto cheese production, with the use of the same trnL-specific couple of primers. The aim was to assess the presence of plant DNA fragments in milk that are consistent with the diet supplied to the animal in accordance with the disciplinary of production. 100 ng of total DNA extracted from each milk sample was used to run a PCR-based reaction with the trnL set of primers. As shown in Fig. 7, the polymorphic pattern of the amplification products obtained from milk is consistent with that present in the coresponding feed. Again, soy (307bp band) was undetectable in both the milk and the feed samples. Additional faint bands that could represent some grazed grass species are also detectable.
Figure 7. Agarose gel electrophoresis of PCR products amplified with the trnLfw3/trnLrw2 set of primers from 100 and 200 ng of total DNA extracted from two milk samples used for Bitto PDO cheese production (Hm, Fm), from their corresponding feed (Hd and Fd) and from the reference single plant species mixed together (WMBS). Mk: molecular sizes marker. Co- : negative control.
CONCLUSION
The aim of this contribution was to show that novel molecular approaches, based on intron length polymorphism, resulting from the combined detection of plant nuclear and chloroplast genes can be successfully used to trace the feed chain that leads to milk production. We have used the TBP-based methods for the rapid and convenient identification of plant species in feed, forages and grasses. TBP is a very effective marker that allows for the genotyping of almost any plant species and this is of real help when dealing with not previously characterized genomes. Since TBP-based methods can successfully work also on DNA extracted from plant mixtures, this also helps to identify contaminants or undesired 234 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al. components that may also result from deliberated adulterations. However, TBP is a nuclear- based marker. As such it is unsuccessful in the detection of traces of plant DNA in animal products, namely milk. This is the reason why we explored the possibility of using chloroplast-based markers such as matK, rbcL and ultimately trnL, as more reliable diagnostic tools. In fact, although all of them are single-copy genes, they are present in multiple copies in the plant cell, the number of which corresponds to the number of plastids, usually ranging from 5000 to 50000 per cell. trnL is by far the most useful of these markers since plant components can be distinguished on the sole basis of a simple PCR reaction because, as it is the case for TBP, any species is characterized by a different length of the intron present in the gene. Intron Length Polymorphism (ILP) is in fact a very powerful and easy technique able to uncover differences at species level and often at lower levels such as subspecies and varieties. ILP markers are also codominant, neutral and stable. Finally, approaches based on ILPs inheritably contribute to a very important aspect of basic science that is related to the role of introns in the course of eukaryotic evolution and speciation. It is indeed a fact that in our years-long experience, we have always found that differences in intron length strictly correlate with species distinctiveness as if the occurrence of a new species has to associate to changes in introns. This is a really intriguing issue that can lead very far, up to the original genetic determinants of Eukaryotes in the effort of establishing how, when and why the splicesomal machinery came to light. The strict relation between ILP and Eukaryotes speciation, although unclear in its functional meaning at the moment, seems to deny speculations about the presence of an intelligent design.
Chart 1. Correspondence between the declared composition of 5 commercial feeds and the individual components detected by either the TBP or the trnL-based methods. W: wheat; B: barley; M: maize; S: soy. Components are reported in descending order with respect to the label (declared) or to the level of detection (found). Discrepancies are indicated in the last two columns. nd: not determined.
FEED FEED COMPOSITION CORRESPONDENCE DECLARED FOUND TBP trnL TBP trnL D M, B, W, S M, B,W M, B,W -S -S F M, B, S W, M, B W/B, M -S -S H M, B, W, S W, M, B W/B, M -S -S L M, S S, M nd ok nd P S, B, W, M NT M,W, S nd -B
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covalently linked to mouse DNA. Proceedings of the National Academy of Sciences of the United States. 1997 94, 961-966. [32] Beever, D.E; Kemp,CF. Safety issues associated with the DNA in animal feed derived from genetically modified crops. A review of scientific and regulatory procedures. Nutrition Abstracts and reviews, Series B: Livestock Feeds and Feeding 2000 70, 175- 182. [33] Jonas, DA; Elmadfa, I;. Engel, KH; Heller, KJ; Kozianowski, G;. König, A; Müller, DJ; Narbonne, F; Wackernagel, W;. Kleiner J. Safety considerations of DNA in food. Annals of Nutrition and Metabolism. 2001 45, 235–254. [34] Einspanier, R; Klotz, A; Kraft, J; Aulrich, K; Poser, R; Schwägele, F; Jahreis, G; and Flachowsky G. The fate of forage plant DNA in farm animals: a collaborative case- study investigating cattle and chicken fed recombinant plant material. European Food Research and Technology. 2001 212, 129–134. [35] Klotz, A; Einspanier, R.. Development of a DNA-based screening method to detect cow milk in ewe, goat and buffalo milk and dairy products using PCR-LCR-EIA-technique. Milchwissenschaft, Milk science International. 2001 56, 67–70. [36] Jennings, JC; Whetsell, AJ; Nicholas, NR; Sweeney, BM; Klaften, MB; Kays, SB; Hartnell, GF; Lirette, RP; Glenn, KC. Determining whether transgenic or endogenous plant DNA is detectable in dairy milk or beef organs. Bulletin of the International Dairy Federation. 2003 383, 41-46. [37] Poms, RE; Hochsteiner, W; Luger, K; Glössl, J; Foissy, H. Model studies on the detectability of genetically modified feeds in milk. Journal of Food Protection. 2003 66, 304-310. [38] Castillo, AR; Gallardo, MR; Maciel, M; Giordano, JM; Conti, GA; Gaggiotti, MC; Quaino, O; Gianni, C; Hartnell GF. Effects of feeding rations with genetically modified whole cottonseed to lactating dairy cows. Journal of Dairy Science. 2004 87, 1778- 1785. [39] Phipps, RH; Deaville, ER; Maddison, BC. Detection of transgenic and endogenous plant DNA in rumen fluid, duodenal digesta, milk, blood, and feces of lactating dairy cows. Journal of Dairy Science. 2003 86, 4070-4078. [40] Nemeth, A; Wurz, A; Artim, L; Charlton, S; Dana, G; Glenn, K; Hunst, P; Jennings, J; Shilito, R; Song, P. Sensitive PCR analysis of animal tissue samples for fragments of endogenous and transgenic plant DNA. Journal of Agricultural and Food Chemistry. 2004 52, 6129-6135. [41] Ponzoni, E; Gianì, S; Mastromauro F; Breviario D. From milk to diet: feed recognition for milk authenticity. Journal of Dairy Science. 2009 92, 5583-5594. [42] Ponzoni, E; Mastromauro, F; Gianì, S; Breviario, D. Traceability of plant diet contents in raw cow milk samples. Nutrients. 2009, 1, 251-262. [43] Rizzi, A.; Brusetti, L; Arioli, S; Nielsen, KM; Tamburini, A; Sorlini, C; Daffonchio, D. Detection of feed-derived maize DNA in goat milk and evaluation of the potential of horizontal transfer to bacteria. European Food Research and Technology, SpringerLink Journal. 2008 277, 1699-1709. [44] Phipps, R.H., Jones, AK; Tingey, AP; Abeyasekera, S. Effect of corn silage from an herbicide-tolerant genetically modified variety on milk production and absence of transgenic DNA in milk. Journal of Dairy Science. 2005 88, 2870–2878. 238 Silvia Gianì, Anna Paola Casazza, Luca Braglia et al.
[45] Vogel, J; Hubschmann, T; Borner, T; Hess, WR. Splicing and intron-internal RNA editing of trnK-matK transcripts in barley plastids: support for matK as an essential splicing factor. Journal of Molecular Biology. 1997 270, 179–187. [46] Tsai, L; Yu, Y; Hsieh, H; Wang, J; Linacre, A; Lee, J. Species identification using sequences of the trnL intron and the trnL-trnF IGS of chloroplast genome among popular plants in Taiwan. Forensic Science International, 2006 164 (2), 193-200. [47] Taberlet, P; Gielly, L; Pautou, G; Bouvet, J. Universal primers for amplification of 3 non-coding regions of chloroplast chloroplast DNA. Plant Molecular Biology. 1991 17, 1105–1109. [48] Gielly, L; Taberlet, P. The use of chloroplast DNA to resolve plant phylogenies: Noncoding versus rbcL sequences. Molecular Biology and Evolution. 1994 11, 769– 777. In: Animal Feed: Types, Nutrition and Safety ISBN 978-1-61209-346-8 Editor: Sarah R. Borgearo, pp. 239-246 © 2011 Nova Science Publishers, Inc.
Chapter 13
A COMPARISON STUDY TO DETERMINATE THE BEST MODEL FOR COMPUTING MEAN/MEDIAN IRREGULAR FEED PARTICLE SIZES OF COARSELY DRY-ROLLED BARLEY FOR ANIMAL NUTRITION RESEARCH
L. Du and Peiqiang Yu Department of Animal and Poultry Science College of Agriculture and Bioresources, The University of Saskatchewan 51 Campus Drive, Saskatoon, Canada, S7N 5A8
ABSTRACT
Barley is a main diet ingredient for beef and dairy cattle in North American, particularly in Canada. The particle size distribution and mean and median of barley significantly affects nutrient degradation and availability. The barley is usually coarsely dry-rolled before feeding in North America. The shapes of coarsely dry-rolled barley particles are not round but very irregular. There are three models available in literature which could be used to determine the mean and median particle sizes of a feed with irregular shapes. However, which model is most suitable for coarsely dry-rolled barley is not known. This information is badly needed. The objective of this study was to determine which model was the best model to determine the particle sizes for coarsely dry-rolled barley fed to ruminants. Eighteen barley samples from three consecutive years were used in this study. The models that were compared included: (1) Fisher‘s model; (2) Pond‘s model with 0 mm=100%; (3) Pond‘s model without 0 mm=100%; (4) Model for Geometric Mean (GM). The results showed that RSS from the Pond‘s model with and without 0 mm = 100% were 68.66 and 68.62, respectively. Both values were significantly smaller (P<0.05) than the RSS from the Fisher‘s model (RSS= 363.21), indicating that the Pond‘s model was more suitable to model particle size data from coarsely dry-rolled
Corresponding author: Peiqiang Yu, Ph.D. Ministry of Agriculture Strategic Research Chair: College of Agriculture and Bioresources University of Saskatchewan, 6D10 Agriculture Building, 51 Campus Drive, Saskatoon. Canada, S7N 5A8. Tel: +1 306 966 4132. Fax: +1 306 966 4150. E-mail: [email protected] 240 L. Du and Peiqiang Yu
barley grain than the Fisher‘s model. R2 values (P<0.001) continued to support the point that the Pond‘s models (R2= 0.9987, 0.9984) were better than the Fisher‘s model. Within the Pond‘s methods, no difference was found for RSS and R2 (P>0.05), but better potency was observed in the Pond‘s model with 0 mm = 100%, which included the observation of particles passing through the smallest sieve (0.58 mm). R2 for the Pond‘s model with 0 mm = 100% was 0.9987. The estimation of mean/median particle sizes from Fisher‘s model was larger than those from the Pond‘s and GM models, with GM giving the smallest particle size. In conclusion, the Pond‘s model with 0 mm = 100% was the best model to compute mean/median particle sizes of the coarsely dry-rolled barley expressed as percent cumulative weight oversize. The mean and median particle sizes determined from the Pond‘s model with 0 mm = 100% are best to be used for nutrient availability study in future.
Keywords: Feed Barley processing, coarsely dry-rolled, particle size distribution, mean and median model comparison, ruminants
1. INTRODUCTION
1.1. Research Background and Motivation
Barley is a main diet ingredient for beef and dairy cattle in North American, particularly in Canada. In a beef finishing diet, barley could be added up to 90% of total diet. Barleys are usually coarsely dry-rolled in all North America (Yu et al., 2003; Du et al., 2009). The particle size distribution of barley affects nutrient degradation and availability. Over- processing can lead to an unpalatable ration, reduced dry matter intake and can cause digestive problems (Du et al., 2009). Larger particle size can reduce the surface area for microbial colonization and enzymatic attack. An optimal grain particle size is preferred and required to maximize barley grain digestibility, animal intake and performance (Mathison, 1996; Beauchemin et al., 2001; Du et al., 2009). The shapes of barley particles were not round but very irregular after coarsely dry-rolled. There were three models available in literature which can be used to determine the mean and median particle sizes of a feed with irregular particle shapes, such as Pond Models (Pond et al., 1984) and Fisher‘s Model (Fisher et al., 1988). In the literature, researchers still use geometric mean (GM) diameter to calculate mean particle size with irregular particle shapes. The particle size of coarsely dry-rolled barley samples is not logarithmic-normally distributed. The GM method is only suitable for sphere (or round) particle size with log-normally distribution.
1.2. Objectives of This Research
The objective of this feed study was to determine which model was the best model to determine the particle sizes for barley samples only with irregular particle shapes. The models that were compared included (1) Fisher‘s model, (2) Pond‘s model with 0 mm = 100%; (3) Pond‘s model without 0 mm = 100%; (4) Model for Geometric Mean. The hypothesis is that A Comparison Study to Determinate the Best Model… 241 the best equation suitable for analyzing coarsely dry-rolled barley samples with irregular shapers of particle sizes will have the smallest residue sum of squares (RSS) with the largest coefficient of determination (R2).
2. MATERIALS AND METHODS
2.1. Barley Varieties and Growth Climate Conditions during Three Consecutive Years
Six, two-row barley varieties (AC Metcalfe, CDC Dolly, McLeod, CDC Helgason, CDC Trey, CDC Cowboy) were grown, without irrigation, at the Kernen Crop Research Farm (KCRF), University of Saskatchewan, Saskatoon, SK, Canada and collected during three consecutive years commencing in 2003, 2004 and 2005. All the barleys were managed using the same and standard agronomic production practices for all barley production. Sub-samples of grain collected at harvest were provided by the Crop Development Center (CDC, Brian Rossnagel), University of Saskatchewan, Canada. The information on barley varieties and growth climate condition: highest mean temperatures and rainfall during the three consecutive years were present in Table 1 (Du et al., 2009). The detailed physicochemical characteristics, hydroxycinnamic acids (ferulic acid; FA, 3-methoxy- 4-hydroxy-cinnamic acid; ρ-Coumaric acid: PCA, 4-hydroxycinnamic acid) and their ratio and in situ biodegradability affected by different genotype were reported in Du et al. (2009). The detailed relationship study between physicochemical characteristics and hydrolyzed hydroxycinnamic acids profile of barley varieties and nutrient availability in ruminants were reported in Du and Yu (2010).
Table 1. The variety and growth weather conditions of barley varieties
Growth year Barley varieties1 Barley Barley row Y2003 Y2004 Y2005 Type number 1 AC Metcalfe Malting 2-row 2003 2004 2005 2 CDC Dolly Feed 2-row 2003 2004 2005 3 McLeod Feed 2-row 2003 2004 2005 4 CDC Helgason Feed 2-row 2003 2004 2005 5 CDC Trey Feed 2-row 2003 2004 2005 6 CDC Cowboy Feed (Forage) 2-row 2003 2004 2005 Climate weather condition Highest Mean Temperature 20.9 °C 17.3 °C 17.5 °C Rainfall 190 mm 305 mm 455 mm 1 Six varieties of barley were grown at the Kernen Crop Research Farm (University of Saskatchewan, Saskatoon, Canada). All the barleys were managed using the same and standard agronomic production practices for all barley production (Du et al., 2009; Du and Yu, 2010)
242 L. Du and Peiqiang Yu
2.2. Determination of Mean/Median Particle Size of Barley Grain after Coarse Dry-Rolling
Barley samples were coarsely dry-rolled in a grain roller mill (Sven Grain Mill, Apollo Machine and Products Ltd., Saskatoon, Canada) in the College of Engineering (University of Saskatchewan, Canada) through a 1.55 mm gap (feedlot practice). Particle size distribution of these cracked samples was determined as weight distribution. In brief, triplicate samples (100 g) were sifted into a stack of six test sieves plus one bottom pan arranged in descending sieve aperture sizes (Table 2), fitted in a Ro-Tap sieve shaker (Tyler Industrial Products, OH). The duration of sieving (rotation and tapping) was determined by sieving initially for 1 min and increasing to 5 min until sifting had reached equilibrium according to the American National Standards Institute (American National Standards Institute, 2003) sieving method.
Table 2. Aperture sizes for test sieves for particle size distribution analysis after coarse dry-rolling
Sieve No. Aperture Size (mm) 6 3.36 8 2.36 12 1.70 16 1.19 20 0.84 30 0.58
2.3. Comparison of Three NLIN Models for Determining Mean/Median Particle Size
After sieving, the fractions remaining on each screen were weighed, and particle size distribution was expressed in % cumulative weight oversize by adding up the weight on each sieve and those from all larger screens (American National Standards Institute, 2003). The mean/median particle size values were estimated by fitting these data into two exponential models (Fisher et al. 1988): Fisher‘s equation (Eq.1) and Pond‘s equation (Eq.2) with and without 0 mm = 100%. The particles passing the sieve 0.58 mm were included in Pond‘s equation with 0 mm = 100% and discarded in Pond‘s equation without 0 mm = 100% in the calculation. Data were computed using the NLIN procedure of the Statistical Analytical System (SAS Institute, Inc. 2008). Mean particle size was calculated as the weighted average of sample particle sizes and median particle size was determined to be equivalent to the value at 50% of the percentage cumulative weight oversize. Model 1: Fisher‘s Equation
a R = 100*e-( s -b×s) (Eq. 1),
A Comparison Study to Determinate the Best Model… 243
Model 2: Pond‘s Equation
R = 100*e - k(s w) (Eq. 2),
Mean particle size = 1/k +w, Median particle size = 0.693/k+w, where, R = percentage cumulative weight oversize; s = sieve opening size (mm); w = the smallest predictable particle size; k = the decay constant of the exponential curve describes the proportionality constant between the percent of particles passed to the next sieve and the percent remained; a, b = mathematically estimated parameters. Although the particle size of coarsely dry-rolled barley samples was not logarithmic- normally distributed, geometric mean diameter (GM) (Eq. 3) was calculated for equation comparison because many researchers still use it to calculate mean particle size so far. The average particle size of materials retained on each sieve was fitted into the log-normal distribution curve (American National Standards Institute, 2003) and the geometric mean diameter was calculated accordingly. It needs to be mentioned that GM method is only suitable for sphere (or round) particle size with log-normally distribution. Model 3: Equation for Predicting Geometric Mean Diameter of Log-Normal Distribution:
, (Eq. 3) where, dgw = geometric mean or median particle size of the whole materials, mm, di = nominal sieve opening of the ith screen, mm; Wi = the weight of particles retained on ith sieve, g.
2.4. Statistical Analysis
Statistical analyses were performed using the Proc Mixed procedures in SAS (2008). Treatments were compared by Fisher‘s Protected LSD test. Significance was declared at P < 0.05.
3. RESULTS AND DISCUSSION
3.1. Comparison and Selection of the Best Model for Determination of Mean/Median Particle Sizes of Coarsely Dry-rolled Barley Grain with Irregular Shapes
Particle size distribution data (percentage cumulative weight oversize) was analyzed with three equations using the Gauss-Newton nonlinear iterative method. The statistical parameters 244 L. Du and Peiqiang Yu used for equation comparison and selection of the best model were residue sum of squares (RSS) and coefficient of determination (R2). Fisher et al. (1988) only adopted RSS for their comparative study, while Pasikatan et al. (1999) used four indices to test eight distribution models for ground wheat. The best equation suitable for analyzing coarsely dry-rolled barley samples with irregular shaper of particle size should give the smallest RSS value, with the largest R2 value. In Table 3, the parameters of RSS and R2, as well as the mean/median particle size predicted from Fisher‘s equation, Pond‘s equation (with and without 0 mm = 100%) and geometric mean diameter (GM) calculation equation were compared.
Table 3. Three model accuracy comparison for particle size distribution (mean/median) for coarsely dry-rolled barley grain: Comparison of Fisher‟s model vs. Pond‟s model (with 0 mm = 100%) vs. Pond‟s model (without 0 mm = 100%) in RSS and R2
Mean Median Models RSS R2 (mm) (mm) Model 1: Fisher‘s 3.55 a 3.09 a 363.21 a 0.9917 b Model 2: Pond‘s (with 0 3.35 b 2.91 b 68.66 b 0.9987 a mm=100%) Pond‘s (without 0 mm=100%) 3.35 b 2.91 b 68.62 b 0.9984 a Model 3: Geometric Mean 2.75 c (GM) SEM 0.053 0.045 8.049 0.00049 P value <0.05 a, b, c, d Different superscripts of in the same column are significantly different (P < 0.05). SEM=standard error of means.
RSS from Pond‘s equation with and without 0 mm = 100% were 68.66 and 68.62, respectively, not significantly different (P>0.05) between the two. However, both values were significantly smaller (P<0.05) than RSS from Fisher‘s equation (RSS=363.21), indicating that Pond‘s equation was more suitable to model particle size data from coarsely dry-rolled barley grain than the Fisher‘s equation. R2 values (P<0.001) continued to support the point that Pond‘s equation (R2= 0.9987, 0.9984) was better than the Fisher‘s equation. Within Pond‘s methods, no difference was found for RSS and R2, but better potency was observed in Pond‘s equation with 0 mm = 100%, which included the observation of particles passing through the smallest sieve (0.58 mm). R2 for Pond‘s equation with 0 mm = 100% was 0.9987. The estimation of mean/median particle size from Fisher‘s equation was larger than those from Pond‘s and GM calculation equation, with GM giving the smallest particle size. Since the two parameters (RSS, R2) denoted that the Pond‘s equation was the best choice, in the final calculation and comparison in this study, the Pond‘s equation with 0 mm = 100% was applied for computing mean/median particle sizes of coarsely dry-rolled barley samples expressed as percent cumulative weight oversize.
A Comparison Study to Determinate the Best Model… 245
3.2. Mean/Median Particle Size of Coarsely Dry-rolled Barley Grains: Magnitude of Difference and Genotypic Variation
Table 4 showed the mean and median irregular particle sizes of the coarsely dry-rolled barley, reported by Du et al. (2009). The published results showed that barley variety exerted a significant effect on the mean/median particle size estimated from Pond‘s equation with 0 mm = 100%. The range of mean particle size estimated using Pond‘s equation was from 3.06 to 3.66 mm with an average value of 3.35 mm. Median particle size behaved similarly ranging from 2.71 to 3.04 mm and an average of 2.91 mm. Numerically, the predicted sequence for mean particle size from large to small was CDC Cowboy, CDC Helgason, McLeod, CDC Dolly, AC Metcalf, CDC Trey, while in the rank of median particle size, it was CDC Cowboy, CDC Helgason, CDC Dolly, McLeod, AC Metcalf, and CDC Trey (Du et al. 2009).
Table 4. Genotypic difference and Variation in the mean and median particle sizes of coarsely dry-rolled six barley varieties collected during three consecutive years, predicted by Pond‟s equation with 0 mm = 100% (Source: Du et al., 2009)
Particle size distribution
No Barley variety Mean Median (mm) (mm) 1 CDC Cowboy 3.66 a 3.04 a 2 CDC Helgason 3.39 ab 2.98 a 3 McLeod 3.35 bc 2.92 a 4 CDC Dolly 3.33 bc 2.94 a 5 AC Metcalf 3.31 bc 2.84 ab 6 CDC Trey 3.06 c 2.71 b SEM 0.073 0.047 Mean 3.35 2.91 a, b, c, d Different superscripts of in the same column are significantly different (P < 0.05). SEM=standard error of means.
CONCLUSIONS AND FUTURE RESEARCH
In conclusion, the Pond‘s model with 0 mm = 100% was the best model and should be applied for computing mean/median irregular particle sizes of coarsely dry-rolled barley expressed as percent cumulative weight oversize. The correlation between mean and median irregular particle sizes and nutrient utilization and availability of barley in ruminants needed to be future investigated.
246 L. Du and Peiqiang Yu
ACKNOWLEDGMENTS
The authors wish to thank Crop Development Center (Professor Dr. Brian Rossnagel) for providing various varieties of barley samples, and B.G. Rossanagel, J.J. McKinnon and D.A. Christensen for valuable suggestions and discussion, the research staff (Z. Niu) in Department of Animal and Poultry Science, University of Saskatchewan, Canada, for helpful assistance and chemical analysis, ADF-Saskatchewan for financial support.
REFERENCES
American National Standards Institute (ANSI). 2003. Method of determining and expressing fineness of feed materials by sieving. ASAE Standard. ANSI/ASAE S319.3. Beauchemin, K. A., Yang, W. Z. and Rode, L. M. 2001. Effects of barley grain processing on the site and extent of digestion of beef feedlot finishing diets. J. Anim. Sci., 79: 1925- 1936. Du, L., P. Yu, B.G. Rossnagel, D.A. Christensen, J.J. McKinnon. 2009. Physicochemical characteristics, hydroxycinnamic acids (ferulic acid, ρ-coumaric acid) and their ratio and in situ biodegradability: Comparison genotypic differences among six barley varieties. J. Agric. Food Chem. 57 (11): 4777–4783. Du, L., P. Yu. 2010. Relationship of physicochemical characteristics and hydrolyzed hydroxycinnamic acids profile (ferulic acid, p-coumaric acid, their Rratio) of CDC barley varieties and nutrient availability in ruminants. J. Cereal Sci. (Elsevier). In press. Fisher, D. S., Burns, J. C. and Pond, K. R. 1988. Estimation of mean and median particle size of ruminant digesta. J. Dairy Sci., 71: 518-524. Mathison, G. W. 1996. Effects of processing on the utilization of grain by cattle. Anim. Feed Sci. Technol., 58: 113-125. Pasikatan, M. C., Steele, J. L., Milliken, G. A., Spillman, C. K. and Haque, E. 1999. Particle size distribution and sieving characteristics of first-break ground wheat. An ASAE meeting presentation. St. Joseph, MO. Pond, K. R., Tolley, E. A., Ellis, W. C. and Matis, J. H. 1984. A method for describing the weight distribution of particles from sieved forage. Pages 123-134 in P. M. Kennedy, ed. Techniques in particle size analysis of feed and digesta in ruminants. Canadian Society of Animal Science, Edmonton, AB. SAS. 2008. User's guide: Statistics, 9th ed. SAS Institute Inc., Cary, NC. Yu, P., Meier, J. A., Christensen, D. A., Rossnagel, B. G. and McKinnon, J. J. 2003. Using the NRC-2001 model and the DVE/OEB system to evaluate nutritive values of Harrington (malting-type) and Valier (feed-type) barley for ruminants. Anim. Feed Sci. Technol., 107: 45-60.
INDEX
aging process, 157 # agonist, 64, 80, 123 agriculture, 24, 56, 72, 80, 91, 101, 103, 108, 120, 20th century, 1 190 21st century, 126 albumin, 20, 115, 122 A aldehydes, x, 155, 157, 158 alfalfa, 15, 18, 46, 51, 55, 56, 57, 58, 217 absorption spectroscopy, 36 algae, vii, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 16, 17, 18, abstraction, 159 19, 20, 21, 22, 25, 26, 29, 30 access, 40, 62 algorithm, 129, 136, 137, 138, 139, 142, 143, 149, accessibility, 46 154 accessions, 223 alkaloids, 88, 104 accounting, 176, 179 allergic reaction, 90 accurate models, ix, 128 alternative energy, xi, 196 acid, x, xi, 8, 10, 20, 27, 35, 36, 37, 47, 48, 49, 53, alters, 193 54, 56, 57, 58, 66, 72, 88, 94, 97, 102, 105, 106, American Heart Association, 66 113, 116, 120, 121, 122, 123, 124, 125, 155, 157, amines, 185, 188, 189, 192 158, 161, 162, 163, 164, 168, 183, 184, 185, 187, amino, ix, 2, 8, 20, 22, 28, 54, 64, 66, 75, 77, 107, 188, 189, 190, 191, 192, 193, 194, 208, 210, 215, 113, 115, 116, 120, 121, 123, 124, 155 225, 236, 241, 246 amino acid, ix, 2, 8, 22, 54, 66, 107, 113, 115, 116, acidosis, 46, 52 120, 121, 123, 124, 155 active compound, 17, 21 amino acids, ix, 2, 22, 66, 107, 113, 115, 116, 121, adaptation, xi, 40, 95, 113, 145, 148, 196, 198, 202 123, 155 adaptations, 199, 203, 205 aminoglycosides, 96 additives, vii, ix, 51, 62, 86, 87, 107, 108, 109, 110, ammonia, 8, 17, 19, 113, 215 115, 116, 121, 122, 164, 171, 191, 220 amphibians, 66 adhesion, 16, 29, 30 amylase, xi, 35, 56, 196, 199, 203, 205, 210 adipose, 92, 160, 161, 190 anaerobic bacteria, 29, 94 adipose tissue, 92, 160 anatomy, 205, 206 adsorption, 70 animal husbandry, 86, 99, 109 advancement, vii, 33 anisotropy, 167 advancements, 2 annealing, 231, 232 adverse conditions, 128 anorexia, 98 adverse effects, 14, 68, 86, 90 anthropology, 235 aflatoxin, 88, 89, 102, 171 antibiotic, 22, 29, 67, 70, 72, 73, 75, 76, 77, 80, 82, Africa, 108, 196, 204, 206 83, 91, 95, 96, 105, 110, 120, 122, 124, 126, 184, age, 89, 109, 114, 120, 122, 158, 164, 209 191, 194 248 Index antibiotic resistance, 67, 76, 77, 80, 83, 91, 95, 105, bias, 40, 142, 152, 231 110, 124 bicarbonate, 36 antibody, 15, 20 bile, 111, 160, 161 anticoagulant, 12, 21, 23, 27, 31 bioaccumulation, 70 antioxidant, vii, x, 1, 2, 12, 14, 16, 19, 20, 23, 24, 29, bioactive compounds, vii, 1, 2, 17, 18, 156 31, 156, 158, 159, 160, 161, 162, 163, 164, 166 bioavailability, 31, 160, 161, 164, 167, 184 apoptosis, 158, 159 biochemistry, 224 appetite, 117, 184 biodegradability, 47, 54, 241, 246 aquaculture, viii, 2, 51, 61, 65, 66, 67, 71, 73, 74, 76, biodegradation, 47, 54, 58, 59, 71, 78, 114 77, 78, 79, 80, 81, 82, 83 biodiversity, 108 Argentina, 183 biogeography, 206 arginine, 116 biological activities, 3, 5, 11, 12, 21, 87, 162 arthritis, 66, 97 biological activity, 5, 8, 123 ascorbic acid, 158 biological responses, vii, 1 Asia, 65, 73, 82, 108, 119 biological systems, ix, 155, 156 aspartate, 20 biological weapons, 89 aspergillosis, 98 biologically active compounds, 21 assessment, 23, 62, 64, 65, 68, 70, 72, 76, 78, 83, 86, biomarkers, 224 90, 91, 99, 101, 104, 105, 130, 150, 165, 167, biomass, 208, 217 170, 174, 180, 222 biopolymer, 44, 45, 51, 54, 59 assimilation, 160 biopolymers, 45 asthma, 66 biosynthesis, 165, 206 asymptomatic, 94, 97 biotechnology, 120 atherosclerosis, 157, 187, 192 birds, 93, 98, 101, 105, 109, 204 atoms, 156, 161, 163 blood, 12, 14, 15, 19, 20, 25, 124, 192, 193, 225, Australasia, 30 236, 237 authenticity, xii, 219, 220, 237 blood urea nitrogen, 20 awareness, 63, 98 bloodstream, 236 body composition, 192 B body fat, 187, 192 body size, 204 Bacillus subtilis, 191 body weight, xi, 11, 15, 40, 65, 183, 184, 186 bacteria, viii, xi, 5, 6, 7, 8, 11, 12, 18, 26, 27, 29, 31, brain, 66, 82, 83, 92 67, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, breakdown, 37, 50, 160 85, 86, 87, 91, 92, 93, 94, 95, 96, 97, 102, 103, breeding, xi, 34, 35, 66, 109, 172, 195, 196, 203 105, 106, 118, 124, 125, 126, 149, 151, 183, 184, Britain, 216, 217 185, 186, 188, 189, 191, 192, 194, 201, 203, 205, Brown algae, vii, 1, 2 208, 215, 237 buffalo, 237 bacterial fermentation, 113 businesses, 220 bacterial infection, 71 butylated hydroxyanisole (BHA), x, 155, 159 bacterial pathogens, vii, 1 butylated hydroxytoluene (BHT), x, 155, 159 bacterial strains, 67, 90, 91, 92 by-products, 50, 51, 52, 55, 90, 221 Bangladesh, 66 barley samples, xii, 239, 240, 243, 244, 246 C basal forebrain, 25 base, ix, 79, 128, 135, 151, 198, 222, 224, 225, 228, cabbage, 95, 202 229, 230, 233, 237 cadmium, 90 beef, xii, 14, 18, 19, 29, 31, 36, 41, 46, 76, 101, 104, caecum, 11, 201 126, 162, 165, 167, 172, 237, 239, 240, 246 calcium, 20, 66, 71, 80, 125 behaviors, 48, 58 calibration, 132 Belgium, 81, 171, 172, 173 cancer, xi, 12, 24, 76, 157, 183, 189, 192 beneficial effect, 14, 113, 114 candidates, 136, 228 benefits, vii, xi, 1, 67, 76, 82, 119, 152, 183, 184 carbohydrate, 36, 37, 39, 41, 42, 46, 47, 52, 57, 58, beverages, 21 117, 193, 208, 215 Index 249 carbohydrates, x, 2, 3, 8, 27, 36, 42, 65, 87, 119, 155, chronic diseases, 162 157, 158, 208, 210, 215 circulation, 130, 160, 161, 162 carbon, 157, 161 classification, 81, 87, 135, 205 carbon atoms, 161 cleaning, 94, 190 carcinogenesis, 187 climate, 2, 30, 71, 87, 88, 197, 241 cardiovascular disease, 79, 157, 188, 193 climate change, 2, 30 carotene, 161, 167 climates, 216 carotenoids, 158, 161, 167 clinical symptoms, 95 casein, 31, 114, 124 clone, 83 categorization, 197 cloning, 223 cattle, xii, 13, 14, 18, 19, 22, 24, 25, 31, 49, 50, 51, Clostridium, viii, 85, 86, 92, 96, 101, 104, 125 52, 54, 56, 57, 69, 76, 88, 90, 94, 95, 97, 100, cluster analysis, 45, 58 101, 104, 105, 106, 110, 113, 117, 124, 161, 172, clustering, 129 199, 203, 216, 237, 239, 240, 246 clusters, 175, 176, 178 CDC, 49, 54, 59, 95, 100, 241, 245, 246 coding, 222, 225, 231, 238 cDNA, 120 codominant, 234 cell biology, 222 colitis, 95, 100 cell culture, 20 collaboration, 106 cell death, 128 collagen, 16 cell line, 24 colon, 11, 29, 101, 157, 192, 201, 202 cell lines, 24 colonisation, 110, 115 cell membranes, 159 colonization, 25, 28, 99, 193, 240 cell organelles, x, 155, 158 combined effect, 152 cell surface, 6, 25 commercial, 28, 50, 53, 77, 101, 104, 111, 113, 128, cellular immunity, 12 164, 186, 202, 221, 224, 227, 229, 231, 232, 234 cellulose, 3, 36, 47, 199, 210, 213, 216 commodity, 128 central nervous system, 97 communication, 53, 62 Centrosema pubescens, xi, 207, 209 communities, 74 cephalosporin, 78, 120 community, 67, 80, 97 certificate, 171 compensation, xi, 173, 174, 176, 195 certification, xii, 171, 181, 219, 224 competition, 46, 186 chain of production, xii, 219, 220 compilation, 65 chain propagation, 160 complement, 23, 32, 109, 175 chain-carrying peroxyl radicals, x, 155, 159 complexity, 136, 144 challenges, 75, 99, 101, 152 complications, 109 cheese, 124, 221, 224, 227, 229, 231, 232, 233, 236 composition, ix, xi, xii, 2, 3, 10, 25, 35, 40, 41, 42, chemical, viii, xi, 4, 5, 6, 17, 22, 25, 28, 29, 35, 37, 45, 46, 47, 48, 49, 51, 55, 57, 58, 107, 108, 110, 38, 45, 47, 48, 50, 51, 52, 53, 57, 58, 59, 61, 62, 113, 119, 158, 164, 183, 186, 190, 192, 195, 202, 67, 68, 70, 71, 83, 86, 87, 89, 90, 91, 94, 98, 99, 203, 207, 212, 213, 215, 218, 219, 221, 222, 224, 104, 108, 147, 157, 186, 202, 204, 207, 215, 224, 227, 228, 232, 233, 234 228, 246 compounds, vii, ix, x, 1, 2, 3, 4, 5, 11, 17, 18, 19, 21, chemical characteristics, 70 22, 27, 46, 51, 67, 68, 70, 74, 75, 78, 80, 83, 86, chemical properties, 87, 89 87, 89, 90, 107, 108, 116, 155, 156, 157, 158, chemicals, viii, 17, 31, 61, 63, 64, 66, 67, 69, 70, 90 159, 161, 162, 185, 198, 202, 224, 236 chemotherapy, 76 computation, 136 chicken, 77, 101, 103, 118, 164, 167, 168, 225, 237 computer, 54 children, 189, 191, 224 computing, 139, 244, 245 China, 65, 66, 76, 83 conjugation, 72, 134, 188 Chinese medicine, 24 conservation, xi, 108, 195, 203, 204, 208, 216 chloroplast, 222, 225, 229, 233, 238 constituents, 23, 47, 171 cholesterol, 12, 157, 166, 167, 193 construction, 135 choline, 116, 120, 121, 124 consumer protection, 220 chromatography, 5, 28, 75, 78, 79, 82 consumers, viii, 61, 65, 67, 189, 220 250 Index consumption, viii, ix, xi, 2, 52, 62, 63, 64, 65, 66, 67, deficit, 66 78, 79, 80, 85, 86, 88, 92, 93, 95, 97, 98, 100, degradation, xii, 11, 18, 26, 36, 37, 38, 39, 41, 42, 108, 110, 128, 140, 186, 196, 197, 198, 220, 222, 43, 46, 47, 49, 50, 54, 55, 56, 58, 59, 69, 70, 71, 224 80, 157, 202, 228, 239, 240 consumption habits, 220 degradation rate, 37, 39 contaminant, 70, 77 dehydrate, 51 contaminated food, 86, 89, 96 denaturation, 48 contaminated water, 91 Denmark, 75, 110 contamination, vii, viii, x, 16, 61, 62, 67, 69, 70, 75, Department of Health and Human Services, 102 76, 85, 86, 87, 90, 92, 94, 95, 96, 97, 98, 99, 103, dependent variable, 133 104, 105, 131, 169, 170, 171, 172, 173, 174, 175, depth, 69, 116, 117 176, 178, 179 derivatives, 115, 161, 162, 225 continuous data, 149 desiccation, 93, 128 control group, 186 destruction, 67, 128, 152, 173 controversial, 161 detectable, 51, 225, 227, 228, 232, 233, 237 convergence, 139, 150 detection, 62, 64, 88, 91, 148, 225, 227, 228, 229, conversion rate, 12 230, 231, 232, 233, 234 copper, 75, 79, 126, 183, 213 developing countries, 65, 73, 74, 108, 120 coronary heart disease, 66, 78, 157, 164, 191 developmental change, 113 correlation, 152, 245 developmental process, 109 correlation coefficient, 152 deviation, 146, 178, 188 cortisol, 20 diarrhea, 102, 113, 119, 124, 183, 189, 194 cosmetic, 1 diet composition, 119 cost, vii, 33, 34, 46, 50, 109, 117, 118, 133, 135, dietary fat, 161, 187, 190, 191 136, 164, 179, 222, 224, 225, 236 dietary fiber, 28, 57 coumarins, 162 dietary intake, 101 covalent bond, 156 dietary supplementation, x, 111, 112, 156, 159, 163, crises, 170, 171, 172, 173, 174, 175, 176, 181 164, 165, 167, 193 crop, 34, 35, 51, 99, 160 diffusion, 65, 160 crops, 57, 86, 91, 117, 118, 208, 237 digestibility, 3, 12, 14, 16, 19, 25, 26, 36, 38, 43, 44, crystallinity, 47 46, 47, 51, 55, 56, 57, 111, 121, 123, 198, 215, cultivation, 171 240 culture, 7, 8, 13, 18, 20, 130, 185, 191, 192 digestion, vii, 1, 8, 16, 26, 28, 30, 31, 37, 43, 46, 47, cycles, 157, 176 48, 54, 56, 57, 109, 111, 112, 113, 119, 121, 122, Cynodon nlemfuensis, xi, 207, 209 161, 184, 185, 203, 205, 246 cysteine, 115 digestive enzymes, 111, 160, 204 cytomegalovirus, 22 dilation, 136, 137, 140 cytoplasm, 4, 94 diluent, 130 cytoskeleton, 235 dimensionality, 136, 144 cytotoxicity, 89 dioxin, 76, 78, 86, 90, 170, 220 dioxin-like compounds, 78, 86, 90 D dioxin-like PCBs, 76 diseases, 66, 86, 87, 88, 91, 92, 93, 94, 95, 97, 98, dairy cattle, xii, 24, 49, 50, 51, 52, 54, 56, 57, 105, 99, 105, 109, 156, 157, 162, 164, 184, 188, 189 239, 240 dispersion, 67, 160, 179 data analysis, 129, 133 distribution, x, xii, 29, 51, 63, 65, 70, 71, 120, 122, data set, x, 136, 148, 151, 169, 175 135, 151, 165, 169, 170, 172, 174, 175, 176, 178, database, 52, 229 179, 180, 196, 198, 204, 220, 239, 240, 242, 243, decay, 134, 243 244, 245, 246 decision-making process, x, 169, 180 diversification, 65 decomposition, x, 77, 132, 135, 155, 158 diversity, 3, 21, 30, 75, 83, 104, 221, 222, 235 defence, 5, 119 DNA, 72, 79, 89, 95, 157, 222, 224, 225, 228, 229, deficiencies, 65, 159 230, 231, 232, 233, 236, 237, 238 deficiency, 115, 116, 148, 160 Index 251
DNA sequencing, 225 environmental conditions, 93, 94, 109, 147 docosahexaenoic acid, 66 environmental contamination, viii, 62, 87, 99 dogs, 95, 97, 98, 102, 110, 199 environmental impact, vii, 33, 34, 46, 75, 77, 79, 81 DOI, 236 environmental protection, 62 dose-response relationship, 99 environmental temperatures, 119 double bonds, 157 enzymatic activity, 24, 37, 79 down-regulation, 118 enzyme, 53, 56, 72, 87, 112, 114, 122, 160, 199, 203 drinking water, 103 enzyme inhibitors, 87, 122 drug delivery, 192 enzymes, 2, 11, 16, 23, 37, 72, 111, 113, 128, 157, drug resistance, 74 158, 160, 184, 186, 204 drugs, viii, 61, 63, 64, 67, 68, 69, 71, 72, 73, 77, 81, eosinophil count, 20 86, 90 eosinophilia, 186 dry matter, xii, 2, 3, 41, 47, 51, 111, 112, 124, 201, eosinophils, 186, 187 207, 208, 221, 240 EPA, 66 drying, 52, 208, 210, 215 epidemic, 76, 176 duodenum, 44, 112 epithelial transport, 117 dynamic systems, 142, 143, 144 epithelium, 30, 186 dynamical systems, 143 equilibrium, 242 ester bonds, 47 E ethanol, 52, 59 ethers, 157 E. coli, viii, 5, 6, 13, 16, 18, 19, 31, 74, 85, 86, 91, Ethiopian plateau, xi, 195, 196, 198 92, 95, 100, 122, 185, 194 etiology, 124, 156 E.coli, 13 EU, 63, 64, 68, 70, 73, 81, 101, 220, 221 ecology, xi, 4, 5, 11, 18, 22, 25, 102, 121, 123, 196, eukaryotic, 222, 234 203, 204, 205, 217 eukaryotic cell, 222 economic development, 65 Europe, 73, 81, 99, 128, 196 economic growth, 108 European Commission, 62, 79, 101 ecosystem, xi, 195, 197, 204 European Parliament, 62, 81, 220, 234, 235 editors, 54, 100, 102, 105, 203, 204, 205 European Union, 62, 81, 110, 184, 191, 235 eicosapentaenoic acid, 66 everyday life, 90 electromagnetic, 45 evidence, 8, 66, 72, 75, 82, 92, 95, 110, 118, 184, electromyography, 112, 116 198, 199, 232 electron, 156, 157, 159 evolution, 72, 78, 95, 120, 199, 204, 222, 234 electrons, 156, 158 excretion, 68, 69, 112, 184, 193 electrophoresis, 102, 225, 232, 233 exons, 223, 225 employment, 65 experimental condition, 228 employment opportunities, 65 experimental design, xii, 40, 207 enamel, xi, 195, 196 exposure, 4, 7, 15, 16, 62, 67, 73, 78, 91, 92, 98, 99, encephalitis, 94 122, 124, 125, 158, 170, 181 encoding, 73 extracellular matrix, 27, 30 endocarditis, 97 extraction, 5, 21, 28, 35, 79, 228 endocrine, 111, 114, 119 extracts, vii, 1, 2, 8, 11, 18, 19, 21, 25, 28, 166, 225 endocrine system, 111 extrusion, 48, 115 endosperm, 55, 58 energy, xi, 16, 31, 36, 43, 44, 47, 49, 50, 52, 56, 57, F 58, 65, 123, 136, 173, 184, 190, 196, 215, 216 energy supply, 190 families, 74, 129, 222, 235 engineering, 53, 117, 118, 129 farmers, 172, 208, 220 England, 57, 165, 166, 167 farming techniques, 66 Enterobacteria, xi, 183, 185 farms, 67, 68, 71, 76, 78, 79, 80, 94, 99, 105, 170, environment, 4, 8, 45, 48, 66, 67, 68, 69, 70, 71, 72, 172, 174, 178, 179, 226, 229, 230 73, 75, 76, 77, 78, 80, 91, 92, 93, 94, 96, 98, 101, 108, 111, 113, 123, 152, 154, 162, 170, 184 252 Index fat, ix, 15, 35, 41, 49, 64, 90, 92, 116, 120, 155, 157, formation, x, 23, 46, 71, 80, 121, 155, 157, 158, 159, 158, 161, 165, 172, 173, 184, 187, 188, 190, 191, 161, 188, 204 192, 193, 221, 228, 236 formula, 36, 111, 125, 137 fat reduction, 187 fossils, 204 fat soluble, 158 fouling, 5, 29, 31, 79 fatty acids, xi, 17, 24, 66, 76, 78, 79, 82, 83, 111, fragments, 87, 95, 115, 224, 225, 228, 229, 231, 232, 124, 157, 160, 162, 164, 183, 184, 188, 190, 193, 233, 237 236 France, 64, 76, 81, 172, 221 feces, xi, 69, 71, 101, 189, 196, 197, 199, 201, 236, fraud, 222 237 free radicals, ix, 93, 155, 156, 157, 158, 159, 160, federal government, 35, 53 161, 162 feed additives, vii, ix, 86, 107, 108, 109, 110, 115, freezing, 104 116, 121, 122 frequency resolution, 133 feedstock, 52 freshwater, 9, 78, 121 feedstuffs, vii, 33, 34, 35, 43, 55, 56, 90, 117 fruits, xi, 5, 95, 161, 163, 167, 195, 197, 224 fermentable carbohydrates, 119, 208 FTIR, 45, 48, 58, 59 fermentation, xi, 8, 16, 17, 18, 26, 27, 31, 42, 43, 44, fucodians, vii, 1 47, 48, 52, 55, 56, 57, 111, 113, 122, 196, 198, functional food, xi, 183 199, 201, 202, 205, 217 funding, 35 ferritin, 158 fungal infection, 98 fertility, 70, 172, 208 fungi, 18, 26, 54, 87, 88, 89, 91, 92, 98 fiber, xi, 24, 28, 31, 35, 47, 57, 195, 202 fibers, 56, 65, 196 G filiform, 199 GABA, 120 filtration, 36 gastrointestinal tract, vii, ix, 30, 89, 96, 97, 107, 109, financial, 179, 246 112, 114, 115, 122, 124, 190, 201, 236 financial support, 246 gastroparesis, 125 fish, 50, 65, 66, 67, 68, 71, 73, 74, 75, 76, 78, 79, 80, gel, 102, 232, 233 82, 90, 110, 128 gelada feces, xi, 196, 201 fish oil, 67, 76, 79, 90 Geladas, xi, 195, 196, 197 flavonoids, x, 156, 159, 161, 162 gene expression, 159 flavour, 128, 164, 224 gene transfer, 72, 75, 82, 91, 105 flora, 30, 97, 124, 189 genes, 46, 73, 74, 77, 80, 81, 91, 95, 102, 118, 164, flowers, 161, 224 194, 198, 222, 225, 228, 229, 233, 235 fluctuations, 126 genetic components, 224 fluid, 46, 87, 130, 159, 236, 237 genetic diversity, 83, 222, 235 fluoroquinolones, 72, 73, 91 genetic linkage, 73 food additives, 87 genetic marker, 231 food chain, viii, 62, 63, 72, 75, 86, 90, 220, 222 genetic traits, 73 food habits, 65 genome, 103, 104, 224, 226, 235, 238 food industry, ix, 63, 118, 128, 152, 220 genotype, 46, 49, 54, 59, 104, 241 food poisoning, 82, 91, 96, 97 genotyping, 226, 233 food production, 5, 63, 74, 90, 92, 96, 108, 119 genus, 88, 93, 94, 96, 97, 100, 102, 196, 204, 205, food products, 64, 65, 67, 90, 91, 96, 97, 99, 165, 206, 226 192, 226, 235 geographical origin, 220, 221, 226, 235 food safety, viii, ix, x, 62, 63, 66, 76, 81, 93, 99, 101, Germany, 102, 106, 130, 131, 172, 195, 196, 202, 102, 104, 106, 128, 169, 170, 180, 181, 220, 234 207, 210 food security, 62, 65, 77, 82, 117 global climate change, 2 food web, 67, 75 global demand, 108 forage crops, 57, 208 global markets, 77, 99 force, 65, 72, 220, 221 global trade, 74 Foreign gene-transformation, vii, 33, 35 global warming, xi, 195, 198, 204 glucose, 10, 11, 12, 20, 121 Index 253 glutathione, 19, 20, 158 horizontal transmission, 93 glycerol, 131 hormone, 89, 110, 111, 116 glycol, 16, 130 hormones, 79, 90 glycopeptides, 73 horses, 88, 95, 96, 97, 100, 110, 196, 199, 201, 205 glycosaminoglycans, 25 host, xi, 2, 27, 28, 87, 88, 89, 113, 183, 184 goat milk, 228, 237 House, 172, 180 grass, xi, xii, 17, 18, 162, 195, 196, 197, 198, 199, housing, 108, 109 201, 202, 203, 204, 207, 208, 209, 211, 214, 217, human, viii, ix, 2, 22, 24, 27, 29, 61, 62, 63, 64, 65, 218, 233, 236 66, 67, 68, 71, 72, 73, 74, 75, 76, 77, 79, 81, 83, grasses, xi, xii, 55, 195, 196, 198, 199, 207, 208, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 98, 99, 102, 209, 214, 216, 219, 221, 226, 233 104, 105, 106, 108, 110, 111, 114, 116, 120, 155, grasslands, 197, 225, 226 156, 157, 162, 187, 188, 192, 202, 203, 220, 222 grazers, 198 human body, 114 grazing, 14, 19, 29, 57, 196, 197, 198, 202, 217, 224, human health, viii, 61, 62, 63, 66, 67, 68, 73, 74, 75, 236 81, 87, 88, 90, 91, 92, 93, 156 Great Britain, 216, 217 human subjects, 116 Greece, 127, 155 humidity, 88, 158, 209 greenhouse, 46 Hunter, 79, 197, 205 groundwater, 70 husbandry, 86, 99, 109, 110, 112, 114 group size, 204 hybrid, 139, 141, 142, 149, 154 grouping, 110 hydrogen, 6, 21, 115, 119, 124, 159, 160, 163 growth factor, 121 hydrogen abstraction, 159 growth rate, 12, 15, 108, 121 hydrogen atoms, 163 growth temperature, 93, 102 hydrogen peroxide, 6, 21, 115, 119, 124 guidelines, viii, 61, 221 hydrogenation, 48 guiding principles, 220 hydrolysis, 23, 114, 160, 184, 199 Guinea, 209 hydroperoxides, x, 155, 157, 161 hydrostatic pressure, ix, 102, 128, 145, 152 H hydroxide, 25 hydroxyl, 6, 159, 160, 161, 163 H. pylori, 11, 12 hydroxyl groups, 6, 161 habitat, 23 hygiene, 94, 97, 114 habitats, 67, 108 hypothesis, 23, 47, 112, 228, 230, 240 haptoglobin, 158 harvesting, 160, 217 I hazards, viii, 61, 62, 63, 73, 90, 92, 94, 98, 105, 152 Health and Human Services, 102 ibuprofen, 78 health condition, 156 identification, ix, xii, 47, 62, 97, 105, 128, 129, 142, health effects, 67, 86, 172 143, 144, 149, 152, 153, 154, 164, 202, 219, 220, health problems, 189 222, 227, 228, 229, 232, 233, 236, 238 health risks, 67 identification problem, 154 health status, 190 image, 45, 51 heart disease, 66, 78, 157, 164, 191 immobilization, 12, 22 heavy metals, 67, 73, 86, 90, 171 immune function, vii, 1, 14, 19, 29, 112 Helicobacter pylori, 11, 29 immune modulation, 187 hemicellulose, 36, 47, 199, 213 immune response, 11, 27, 123, 186, 191, 194 herb roots, xi, 195, 197 immune system, 89, 109, 111, 123, 184, 185, 193 herbicide, 88, 237 immunity, 12, 89, 193 herpes, 22 immunocompromised, 98 herpes simplex, 22 immunomodulation, 89 heterogeneity, 147 improvements, 46, 52, 65 homeostasis, ix, 107, 110, 111, 114 in vitro, xi, 3, 8, 11, 16, 17, 18, 19, 20, 24, 26, 27, homolytic, 157 30, 31, 37, 54, 55, 56, 57, 58, 74, 88, 111, 116, Hong Kong, 165, 166, 167 184, 188, 192, 196, 201, 202 254 Index in vivo, 8, 11, 27, 116, 118, 123, 160, 162, 191 incidence, 57, 73, 89, 93, 96, 97, 99, 112, 113, 119, J 164, 184, 188, 189, 194 Japan, 25, 79, 128, 204 income, 65 jejunum, 116, 117 incubation period, 18 Jordan, 83 incubation time, 38 incubator, 38 K India, 196, 204 indirect effect, 116, 119 KBr, 44 industries, 34, 46, 72, 170, 176, 184 Kenya, 217 industry, vii, ix, x, 27, 33, 34, 35, 46, 52, 53, 57, 63, ketones, x, 155, 157, 158 65, 66, 73, 91, 94, 96, 108, 118, 128, 152, 164, kidney, 88, 89, 92, 116, 117, 124, 126 169, 170, 171, 176, 181, 184, 220 kinase activity, 20 infection, 12, 28, 29, 73, 92, 93, 94, 95, 96, 97, 104, kinetic model, 152 118, 123, 186, 189, 190, 193 kinetic parameters, 131, 145, 146, 147 inflammation, 114 kinetics, 38, 39, 47, 54, 55, 59, 125, 128, 145, 152, inflammatory bowel disease, 192 161 ingest, 109, 187 ingestion, 64, 160, 161, 188 L ingredients, x, 1, 24, 34, 43, 50, 52, 69, 92, 98, 103, 156, 161, 171, 221, 222, 227 laboratory tests, 82 inhibition, 8, 27, 31, 78, 89, 110, 184, 191 lactation, 19, 36, 51, 55 inhibitor, 27 lactic acid, xi, 97, 102, 112, 121, 124, 125, 183, 185, inoculation, 106, 130, 201 189, 191, 192, 194, 208, 215 inoculum, 130 lactose, 13, 25, 41 insects, 87, 91, 117, 118, 125, 197, 202, 203 large intestine, 111, 113, 114, 187, 194, 201 inspections, 64 latent space, 132 institutions, xii, 219, 220 Latin America, 65 insulin, 116, 121 lead, ix, 48, 52, 67, 75, 86, 90, 92, 110, 111, 127, integrity, 158, 159 216, 234, 240 intelligence, 129, 142, 144 leakage, 131, 159 intelligent systems, 154 learning, ix, 128, 129, 133, 135, 136, 138, 139, 141, intervention, 18, 23, 66 142, 143, 148, 149, 153, 154 intervention strategies, 23 learning efficiency, 139 intestinal tract, 11, 19, 86, 92, 95, 97, 102, 121, 184, learning process, 142 188 lecithin, 193 intestinal villi, 117 legislation, 171 intestine, 41, 42, 43, 44, 48, 49, 94, 110, 111, 112, legume, xii, 55, 57, 118, 124, 125, 207, 208, 211, 113, 114, 116, 119, 121, 122, 125, 126, 187, 189, 214 201 Lepidoptera, 125 intoxication, 64, 90, 96 leucine, 225, 229, 231 introns, xii, 219, 222, 223, 224, 225, 234, 236 liberation, 6 invertebrates, xi, 27, 76, 82, 195, 197, 202 light, 45, 59, 71, 158, 161, 234 iodine, 2, 24, 66 lignans, 161 ionization, 71, 78 lignin, 35, 47, 49, 55, 210, 213 ions, 71 linear function, 148 Ireland, 25, 107, 172 linear model, ix, 127, 130, 139, 140 iron, 66, 165, 213 linear systems, ix, 127, 142, 145 irradiation, 94, 115 linoleic acid, xi, 48, 183, 188, 191, 192, 193 irrigation, 241 lipid metabolism, 14 isolation, 5, 22, 28, 236 lipid oxidation, ix, x, 155, 156, 162, 164, 165, 166, issues, x, 67, 72, 165, 169, 170, 180, 237 167 Italy, 12, 64, 76, 130, 206, 219, 221, 230, 232, 236 lipid peroxidation, x, 20, 155, 156, 157, 158, 159, 160, 162 Index 255 lipid peroxides, 159 mechanical stress, 228 lipids, ix, 70, 110, 155, 156, 157, 158, 159, 160, 162, media, 220 164, 166, 187, 192, 224 median, xii, 176, 178, 179, 239, 240, 242, 243, 244, lipoproteins, 157, 192 245, 246 liposomes, 167 medical, 24, 101, 106 liquid chromatography, 5, 75, 78, 79 medication, 66, 82 Listeria monocytogenes, ix, 101, 102, 104, 105, 127, medicine, 24, 64, 68, 73, 74, 75, 87, 98, 105, 110, 128, 130, 145, 147, 148, 150, 152, 153, 154 111 liver, 64, 80, 88, 89, 92, 120, 152, 160, 190, 236 membrane permeability, 161 livestock, vii, xi, 2, 4, 5, 16, 17, 19, 20, 21, 22, 33, membranes, x, 156, 157, 159, 160, 161, 163, 164, 34, 35, 46, 49, 52, 64, 65, 68, 69, 78, 91, 96, 97, 187 103, 109, 171, 172, 173, 176, 183, 184, 186, 189, memory, 144 195, 197, 208, 215, 216, 217 memory function, 144 low temperatures, 93, 94 mental disorder, 66 lumen, 110, 111, 184, 186 Metabolic, 103, 193 lutein, 161 metabolic pathways, 116 lymphocytes, 11, 225 metabolism, vii, 1, 14, 24, 48, 96, 97, 116, 157, 160, lymphoid, 89 161, 164, 165, 216 lymphoid tissue, 89 metabolites, viii, 3, 4, 15, 19, 30, 46, 64, 68, 69, 85, lysine, 2 86, 87, 88, 89, 98, 121, 123, 124, 162, 164, 186 metabolized, 68 M metal ion, 159 metals, 67, 73, 86, 90, 157, 160, 171, 183 machinery, 234 methanol, 5 macroalgae, 27 methodology, vii, 33, 35, 44, 129, 164 magnesium, 20, 71, 80 methyl group, 116 magnitude, 52, 170, 175 methylation, 72 majority, viii, x, 5, 19, 66, 85, 96, 109, 117, 156, 159 mice, 117, 190, 191, 192, 236 malabsorption, 184, 189, 191 microbial communities, 74 mammals, 105, 109, 198, 204 microbial community, 67 management, 62, 64, 65, 73, 87, 99, 102, 103, 106, microbiota, 184, 185, 190 109, 152, 181, 183, 205, 217, 224, 236 micronutrients, 66 mangroves, 2 microorganism, 72, 87, 91, 95, 215 manipulation, 123 microorganisms, ix, xi, 11, 47, 74, 75, 87, 88, 91, 92, mannitol, 10 109, 112, 117, 128, 152, 183, 184, 186, 215, 216 manufacturing, 99 migration, 115 manure, 69, 73, 77, 78, 82, 91, 92, 95, 184 Ministry of Agriculture, vii, 33, 34, 53, 239 mapping, 51, 136 Missouri, 88, 206 marketing, 46, 52, 63, 65 misuse, 72 mass, 4, 46, 64, 75, 78, 79, 82, 108, 201, 224 mitochondria, 157, 159 mass spectrometry, 75, 78, 79, 224 Model for Geometric Mean (GM), xii, 239 mastitis, 94, 97, 105 modelling, ix, x, 127, 129, 133, 139, 140, 144, 150, materials, xii, 90, 124, 161, 171, 207, 209, 216, 243, 152, 153, 154, 169, 171, 175, 204 246 models, ix, xii, 37, 56, 128, 129, 130, 131, 132, 133, matrix, 27, 30, 34, 45, 48, 132, 160, 161 139, 140, 141, 142, 143, 144, 145, 146, 148, 149, matter, iv, xii, 2, 3, 16, 35, 41, 42, 43, 47, 51, 71, 151, 152, 154, 167, 239, 240, 242, 244 111, 112, 124, 135, 199, 201, 207, 208, 221, 240 modifications, 129, 187, 188 MCP, 44, 49 moisture, 208, 215, 221 measurement, 40 moisture content, 208, 215, 221 measurements, 56, 117, 144 molasses, 26, 221 meat, 5, 14, 19, 21, 24, 64, 65, 76, 86, 88, 90, 91, 92, molecular biology, 58 93, 94, 95, 96, 97, 100, 108, 128, 157, 159, 162, molecular mass, 4 164, 166, 167, 168, 171, 181, 184, 187, 188, 189, molecular oxygen, 157 192, 221, 236 256 Index molecular structure, 44, 53, 55, 59, 72 Northern Ireland, 107 molecular weight, 5, 10, 26, 28, 31, 87, 88 nucleic acid, 43, 225 molecules, 71, 74, 75, 83, 114, 116, 117, 156, 157, nucleophiles, 71 158, 159, 161, 162 nucleotide sequence, 222, 229 monounsaturated fatty acids, 188 nucleotides, 222, 231 morbidity, 72, 98, 190 nutrient, vii, viii, xii, 1, 2, 3, 33, 34, 35, 36, 37, 38, morphology, 113, 117, 122, 125, 126, 190, 199 40, 42, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, mortality, 31, 72, 79, 98, 109, 110, 114, 128, 193 57, 85, 86, 114, 116, 118, 119, 162, 191, 208, mortality rate, 110, 128 216, 239, 240, 241, 245, 246 motivation, 50 nutrients, viii, ix, 2, 12, 16, 48, 52, 85, 108, 110, 111, motor activity, 110 115, 116, 119, 155, 159, 160 mRNA, 235 nutrition, iv, vii, viii, ix, x, 4, 27, 33, 35, 48, 54, 57, mucin, 11, 186 58, 59, 61, 62, 77, 100, 101, 108, 109, 114, 116, mucosa, 16, 28, 94, 114, 117, 122, 123, 184, 190, 120, 155, 156, 162, 187 236 nutritional status, 65, 75, 160 multidimensional, 154 multilayered structure, 129 O multiplication, 130 obesity, 199 multivariate calibration, 132 oceans, 30, 65 muscles, 165 oil, 50, 55, 67, 68, 71, 76, 79, 90, 130, 160, 162, 163, muscular mass, 64 165, 166, 167, 172, 173 mutagen, xi, 183, 185, 188 oilseed, 223 mutation, 72 olive oil, 167 mutations, 72 omega-3, 66, 76, 78, 79, 82 mycology, 105 operations, vii, 33, 34 mycotoxins, viii, 85, 86, 87, 88, 89, 92, 98, 99, 103, opioids, 115 106 opportunities, 65, 73, 152 myoglobin, 157 oral cavity, 97 N organ, 119, 120, 122 organelles, x, 155, 158 National Research Council, 56, 58 organic chemicals, 70 natural habitats, 67 organic matter, 16, 42, 43, 71 natural resources, viii, 61 organism, 79, 86, 87, 89, 94, 109, 112, 119, 128, NCTC, 130 156, 157, 158, 160 negative effects, x, 14, 89, 155, 159, 184 organs, 89, 225, 237 nervous system, 27, 97, 172 overproduction, 159 Netherlands, 24, 55, 121, 126, 169, 170, 171, 172, ovulation, 89 173, 175, 176, 180 ox, x, 155, 158 neural network, ix, 127, 129, 133, 135, 139, 142, oxidation, x, 6, 115, 126, 155, 156, 157, 158, 159, 143, 144, 147, 148, 149, 151, 152, 153, 154 162, 164, 165, 166, 167 neural networks, ix, 127, 129, 135, 142, 143, 144, oxidative damage, 158 151, 153, 154 oxidative deterioration, ix, 155, 156, 162 neurons, ix, 25, 127, 130, 133, 136, 144 oxidative reaction, x, 7, 156, 163, 164 neutral, 35, 36, 56, 57, 210, 234 oxidative stress, 14, 19, 20, 30, 159, 164 New Zealand, 99 oxygen, 93, 157, 159, 161 Nigeria, 207, 208, 209, 216, 217 oysters, 128 nitrogen, 20, 22, 31, 55, 57, 112, 123, 184, 193, 215 nodes, 136, 137, 140, 141, 142, 149 P nonlinear dynamic systems, 143 Pacific, 65, 82 nonlinear systems, ix, 127, 142, 143, 144 Pakistan, 99 non-thermal process, ix, 128 pancreas, 112, 124, 126, 157, 190 normal distribution, 243 Panicum maximum, xi, 207, 209 North America, xii, 5, 30, 239, 240 Index 257 parallel, 143, 144, 145 phosphorus, 20, 36, 66, 112, 213, 216 parasite, 23, 46, 186, 189 phosphorylation, 159 parasites, 79, 92, 186 photochemical degradation, 71 Parliament, 62, 81, 220, 234, 235 photodegradation, 71 parotid, 203, 206 photomicrographs, 51 parotid gland, 203, 206 physical characteristics, 51, 211, 214 participants, 174, 220 physical properties, 210, 211 partition, 37, 68, 70 physicians, 91 pasture, xii, 14, 17, 46, 70, 93, 108, 208, 219, 221, physicochemical characteristics, 10, 54, 241, 246 224, 226, 236 physicochemical properties, 70 pastures, 27, 46, 57, 69, 208, 221, 236 Physiological, 102, 165 patents, 118 physiology, xi, 2, 22, 108, 109, 110, 116, 196, 203, pathogenic bacteria, viii, 6, 74, 76, 85, 92, 126, 184, 205 189 pigmentation, 2 pathogens, vii, 1, 5, 16, 18, 19, 21, 67, 73, 74, 82, 83, pigs, xi, 18, 24, 25, 31, 52, 75, 89, 94, 95, 96, 97, 87, 88, 91, 92, 94, 95, 96, 97, 104, 109, 110, 111, 100, 105, 109, 110, 111, 112, 113, 114, 116, 117, 112, 114, 115, 118, 128, 152, 184, 185, 186 118, 119, 120, 121, 122, 123, 124, 125, 126, 161, pathways, 68, 116, 120 165, 173, 183, 184, 185, 186, 188, 189, 190, 191, pattern recognition, 29 192, 193, 194 PCA, 45, 58, 241 plant diseases, 99 PCBs, 76, 90, 172 plant type, xi, 207, 209, 211, 212, 213, 214 PCR, xii, 219, 222, 225, 229, 230, 231, 232, 233, plants, xi, 2, 4, 9, 17, 42, 52, 56, 66, 75, 87, 88, 103, 234, 237 105, 116, 118, 124, 142, 160, 161, 162, 196, 198, penicillin, 111 203, 224, 226, 235, 238 Pennisetum purpureum, xi, 207, 209 plasma proteins, 114, 123 pepsin, 37, 110, 120 PLS, 130, 132, 133, 149, 150, 151 peptide, 110, 115 PM, 104, 105, 211, 212, 213, 214 peptides, ix, 87, 107, 113, 114, 115, 116, 120, 122, pneumonia, 6, 97 125, 126 polar, 93 permeability, 72, 114, 157, 161 polarity, 161, 167 peroxidation, x, 20, 155, 156, 157, 158, 159, 160, policy, 173, 184 162 policymakers, viii, 61 peroxide, 6, 21, 115, 119, 124 pollutants, 67, 79, 86 pesticide, 91 pollution, 90, 108 pests, 118, 204 polyacrylamide, 225 pH, xii, 19, 37, 71, 83, 94, 112, 124, 128, 161, 185, polyamine, 191, 192 207, 210, 212, 214, 215, 216 polyamines, 189 phage, 236 polychlorinated biphenyl, 86, 90, 172 pharmaceutical, 3, 5, 11, 70, 78, 82, 172 polymerase, 205 pharmaceuticals, 66, 68, 70, 71, 75, 80, 83 polymerase chain reaction, 205 pharmacokinetics, 69 polymerization, 4, 7 pharmacology, 27, 77 polymers, 10, 55, 192 pharyngitis, 97 polymorphism, 222, 231, 233, 235, 236 phenol, 163 polymorphisms, 225 phenolic acids, x, 156, 159, 162 polypeptide, 222 phenolic compounds, x, 3, 156, 159, 224 polypeptides, 222 phenotype, 74 polyphenols, 5, 21, 22, 23, 28, 166 phenylalanine, 116 polysaccharide, 9, 16, 25, 31 phlorotannins, vii, 1, 3, 4, 5, 6, 7, 8, 16, 17, 18, 21, Polysaccharides, 55 26, 28, 29, 30, 31 polyunsaturated fat, 66, 82, 157, 160, 162, 164, 187 phosphate, 22, 36, 37, 130 polyunsaturated fatty acids, 66, 82, 157, 162, 164 phospholipid membranes, x, 156, 163 ponds, 73, 81, 83 phospholipids, x, 155, 157, 159, 160, 164 pools, 180 258 Index population, viii, ix, xi, 8, 18, 61, 64, 66, 101, 107, protein structure, 48, 54, 59 108, 111, 119, 125, 128, 132, 142, 145, 146, 148, protein synthesis, 41, 42, 43, 44, 57, 215 183, 184, 185, 189, 199, 210 proteins, x, 3, 6, 8, 22, 24, 30, 37, 56, 59, 65, 66, population density, 132 111, 113, 114, 115, 116, 118, 120, 123, 126, 155, population growth, 64, 108 156, 157, 158, 198, 202, 203, 204, 205, 206, 222, Portugal, 61, 64, 75, 221 224 positive relationship, 215 proteolysis, 26 potassium, 20, 44 proteolytic enzyme, 112 poultry, 5, 51, 65, 89, 90, 92, 93, 94, 95, 96, 97, 99, prototype, 228 100, 101, 104, 109, 110, 111, 120, 128, 152, 162, Pseudomonas aeruginosa, 6 164, 172, 181, 184, 194 public health, 5, 63, 72, 74, 75, 103, 105 poverty, 65, 82, 217 purification, 28, 54, 102, 119, 122 poverty alleviation, 82 poverty reduction, 65 Q predators, 66, 205 quality assurance, x, 169, 175, 176, 178 preservation, 128, 197, 208 quantification, 26, 53, 164 preservative, 41 Queensland, 217 prevention, 89, 91, 94, 96, 98, 109, 158, 159, 160, questionnaire, 176, 178 170, 180, 187, 192 prevention of infection, 96 R primary antioxidants, 162 primary products, 159 radical formation, 158 primate, xi, 195, 196, 197, 198, 199, 201, 202, 204, radicals, ix, x, 93, 155, 156, 157, 158, 159, 160, 161, 205, 206 162, 163, 166 principal component analysis, 45, 58 rainfall, 209, 241 principles, 62, 81, 90, 93, 94, 98, 99, 149, 160, 216, rancid, 157 220, 234 random numbers, 174 probability, x, 62, 104, 140, 169, 170, 172, 174, 175, rape, 221, 223, 230 176, 178, 179, 180, 210 raw materials, 171 probability density function, 140 RDP, 43 probability distribution, x, 169, 170, 172, 174, 175, reactions, x, 7, 71, 90, 156, 163, 164, 231, 232 178, 180 recall, x, 169, 170, 173, 175, 181 probiotic, xi, 95, 106, 183, 184, 185, 186, 188, 189, receptors, 115 190, 191, 192, 193, 194 recognition, 29, 237 probiotics, xi, 183, 184, 186, 191, 193 recommendations, iv, ix, 40, 52, 85 producers, xii, 46, 68, 88, 98, 103, 171, 172, 173, reconstruction, 135 175, 176, 180, 184, 189, 219 recovery, 98, 194, 202 production costs, 110 rectal temperature, 15, 20 profit, 176, 179 rectum, 19, 28 project, 49, 50, 51, 52, 64 recycling, 80, 172 proliferation, 26, 111 redundancy, 134 proline, 198, 205, 206 Registry, 217 promoter, 64, 99, 119, 184 regression, 39, 130, 132, 133, 135, 136, 140, 142, propagation, 129, 142, 157, 158, 160 143, 144, 145, 149, 150, 151, 198 prophylactic, 67, 72, 73, 74, 76, 112 regression equation, 132 prophylactic agents, 67, 72, 74 regression model, 130, 132, 140, 149, 150, 151 prophylaxis, viii, 61, 63 regulations, viii, 61, 62, 64, 170, 176, 181, 221 proportionality, 243 reinsurance, 179 protection, 62, 66, 80, 86, 87, 99, 158, 159, 160, 186, rejection, 22 220, 221, 235 relaxation, 64 protective role, 117 relevance, x, 105, 169, 175 protein components, 50 reparation, 193 protein hydrolysates, 115 reproduction, 75, 80, 164 Index 259 requirements, 40, 42, 46, 62, 70, 81, 108, 109, 111, Salmonella, viii, 5, 6, 13, 22, 23, 24, 73, 74, 76, 78, 153, 159, 162, 171, 198, 215, 220, 224, 234 83, 85, 86, 91, 92, 93, 99, 100, 101, 102, 103, researchers, 38, 162, 184, 240, 243 104, 105, 106, 118, 123, 184, 185, 188, 193 reserves, 160 salts, 10, 20, 160 residual error, 210 saturated fat, xi, 183, 187, 188 residues, viii, 37, 61, 63, 64, 67, 69, 70, 76, 77, 78, saturated fatty acids, 188 80, 82, 86, 88, 89, 91, 160, 162, 184 scaling, 134, 135 resistance, 23, 28, 49, 67, 72, 73, 74, 75, 76, 77, 78, scavengers, 161 79, 80, 82, 83, 90, 91, 93, 95, 96, 99, 105, 110, science, 24, 58, 162, 234, 237 118, 124, 125, 126, 128, 184, 190, 192 scleroderma, 125 resolution, ix, 45, 51, 127, 133, 135, 223, 225, 228, scope, x, 169, 180 236 SCP, 36, 50 resources, viii, 61, 67, 121, 208 secondary metabolites, viii, 3, 4, 30, 85, 88, 89, 98, respiration, 14, 19, 20 162 respiratory problems, 89 secrete, 112 response, ix, xi, 14, 18, 20, 22, 26, 27, 29, 30, 73, 99, secretin, 114 107, 112, 118, 119, 123, 142, 149, 186, 191, 195, secretion, ix, 107, 110, 111, 112, 113, 122, 125, 161, 198, 218, 235 186 restrictions, 187 security, 62, 65, 77, 82, 117 retail, 15, 23, 80, 83, 102, 164, 171 sediment, 67, 68, 70, 71, 73, 79, 82 retardation, 186 sediments, 67, 68, 69, 70, 71, 76, 78, 79, 82 reticulum, 20 seed, 46, 52, 54, 55, 58, 124 reverse transcriptase, 27 selectivity, 87 risk, vii, viii, x, 12, 62, 64, 68, 70, 73, 74, 75, 76, 78, selenium, 66 79, 83, 85, 87, 90, 91, 92, 97, 99, 103, 104, 105, sensitivity, x, 16, 169 112, 156, 162, 169, 170, 171, 173, 174, 175, 176, sequencing, 224, 225 179, 180, 191, 193, 220 serology, 102 risk assessment, 62, 64, 68, 76, 83, 90, 91, 99, 104, serum, 14, 20, 82, 111, 166, 192, 193 105 services, iv, 75 risk factors, 170, 180 sewage, 68, 70, 80 risk management, 62 sex, 23, 89, 158 risk-financing, x, 169, 171 sex differences, 23 risks, viii, 61, 62, 66, 67, 72, 74, 75, 86, 170, 180 shape, 132, 147, 200 RNA, 89, 120, 238 sheep, 9, 16, 17, 22, 25, 28, 41, 55, 57, 69, 88, 94, rodents, 91, 92 95, 97, 100, 105, 117, 161, 199, 203 root, 124, 142, 145, 149, 202 shelf life, x, 15, 21, 156, 159, 164, 165 roots, xi, 74, 195, 197, 203 shellfish, 73, 74 routes, 92, 96, 99, 105 short-chain aldehydes, x, 155, 158 Royal Society, 83 showing, 72, 94, 225 rules, xii, 62, 63, 91, 136, 141, 171, 219, 221 shrimp, 23, 77, 80, 81, 83 rumen microbiology, vii, 1, 28 Sierra Leone, 66 ruminant diets, vii, 1, 50 signal transduction, 158, 159 runoff, 68 signalling, 83 rural development, 217 signals, ix, 127, 133, 135, 142 rural population, 65 signs, 92, 93, 94 silver, 225 S Simien Mountains National Park, xi, 195, 196, 203 simulation, x, 144, 169, 171, 174, 201 safety, iv, vii, viii, ix, x, xii, 61, 62, 63, 66, 76, 81, skin, 20, 89, 96, 111 85, 86, 91, 93, 98, 99, 101, 102, 104, 105, 106, sludge, 70, 80 108, 128, 159, 162, 169, 170, 171, 180, 181, 219, small intestine, 41, 42, 43, 44, 48, 49, 110, 111, 112, 220, 234 113, 114, 116, 119, 122, 125, 126, 187, 189 saliva, 197, 198, 203 smooth muscle, 12, 26 salmon, 75, 76, 77, 78, 79, 82 260 Index smooth muscle cells, 12 structure, x, 4, 11, 16, 23, 34, 44, 45, 46, 48, 51, 53, social acceptance, 52 54, 55, 58, 59, 71, 75, 80, 87, 89, 129, 132, 133, society, 170 135, 136, 137, 139, 140, 149, 155, 158, 174, 222 sodium, 20, 25, 35, 121, 210 subjectivity, x, 169, 180 sodium hydroxide, 25 substitution, 76 software, 45, 132, 150 substrate, 11, 18, 202 soil particles, 71 substrates, 157, 159, 186, 201 soil type, 71, 216 sulfate, 9, 28 solubility, 16, 48, 70, 161 sulfonamide, 79, 81 solution, 10, 37, 130, 131, 153 sulfonamides, 78 sorption, 70, 71, 83 supervision, 64, 81 South Africa, 79 supplementation, ix, x, 11, 13, 15, 18, 20, 22, 23, 26, South America, 73 57, 107, 111, 112, 116, 126, 156, 159, 162, 163, soybeans, 120 164, 165, 166, 167, 185, 190, 192, 193 Spain, 29, 64, 80, 82, 204 suppliers, 171, 222 species, viii, xi, xii, 2, 3, 4, 5, 8, 10, 11, 12, 16, 18, supply chain, 62, 170, 171, 181 23, 24, 30, 46, 55, 61, 62, 67, 68, 72, 79, 80, 82, suppression, 192 83, 85, 87, 89, 92, 93, 94, 95, 96, 97, 98, 99, 101, surface area, 38, 240 102, 114, 116, 117, 156, 158, 159, 164, 176, 184, survival, ix, 66, 75, 102, 104, 128, 129, 130, 131, 189, 195, 196, 197, 198, 199, 201, 203, 204, 142, 145, 146, 147, 148, 149, 150, 151, 152, 197, 205,뫰207, 208, 210, 211, 212, 213, 219, 221, 198, 204 222, 224, 225, 227, 228, 229, 230, 231, 232, 233, survival rate, 66 234, 235 survivors, 128 specifications, 170, 180 susceptibility, 56, 77, 91, 94, 157, 162 spectral techniques, 55 sustainability, 117 spectrophotometry, 36 Sweden, 110, 122 spectroscopic techniques, 54 Switzerland, 105, 120 spectroscopy, 36, 44, 55 symptoms, 86, 87, 89, 94, 95, 97, 98, 117, 184 spore, 92, 93, 94 synchronization, 56 stability, x, 14, 52, 142, 156, 162, 164, 165, 166, 186 synchrotron-based molecular nutrition research, viii, stabilization, 160 33 stakeholders, 180, 220 syndrome, 95, 99, 102, 183 standard deviation, 146, 178, 188 synergistic effect, 184 standard error, 142, 244, 245 synthesis, 41, 42, 43, 44, 50, 57, 89, 118, 120, 135, Staphylococcus, viii, 6, 23, 25, 73, 85, 86, 92, 96, 99, 153, 159, 215 101, 103, 104 systemic immune response, 194 starch, xi, 9, 35, 37, 38, 39, 42, 50, 52, 56, 190, 196, T 199, 203 starch polysaccharides, 9 Taiwan, 238 state, 110, 137, 144, 148, 192, 200 tannins, 4, 5, 6, 26, 27, 30, 31, 161, 162, 198, 206 states, 64, 66, 68, 161 target, 67, 68, 72, 75, 79, 80, 88, 108, 109, 125, 137, statistics, 65, 176, 178, 179 215, 230 steel, 130 taxa, 199, 201 sterile, 71, 130, 131 taxonomy, 153, 204, 206 sterols, 157 TBP, 222, 223, 224, 225, 226, 227, 228, 232, 233, stomach, 110, 111, 112, 113, 117, 157, 200, 201 234, 235, 236 stomatitis, 22 technical support, 62 storage, viii, 9, 85, 87, 90, 96, 115, 159, 165, 166, techniques, vii, xii, 29, 33, 35, 46, 52, 53, 54, 55, 59, 171, 208, 218 66, 109, 110, 129, 133, 142, 197, 203, 219, 224 streptococci, 97, 103 technological advances, 65 stress, 14, 15, 19, 20, 24, 26, 28, 30, 31, 66, 105, technologies, 34, 50, 108, 118 109, 145, 147, 148, 158, 159, 164, 168, 228 technology, 48, 51, 53, 118 stressors, 20 Index 261 temperature, ix, 2, 10, 14, 15, 19, 20, 28, 48, 59, 71, treatment, ix, xi, 16, 17, 25, 38, 45, 50, 62, 69, 71, 88, 93, 94, 100, 102, 128, 130, 131, 158, 209, 73, 80, 83, 87, 89, 91, 93, 96, 102, 105, 117, 118, 210, 215, 228, 231 119, 128, 130, 131, 145, 146, 147, 148, 150, 152, tension, 157, 161 153, 183, 185 terpenes, 17 trial, 40, 121, 144, 149, 185, 186 tert-butylhydroquinone (TBHQ), x, 155, 159 triglycerides, 157, 160 test data, 151 trypsin, 112, 115 testing, viii, 20, 85, 133, 149, 150, 231 tetracyclines, 71, 74 U texture, 40, 51 UK, 23, 26, 28, 36, 102, 121, 123, 130, 206, 217 Thailand, 74, 82 UN, 107, 125 therapeutic use, 68 uniform, 164, 209 therapeutics, 63 United, 27, 62, 65, 68, 100, 101, 103, 104, 107, 123, therapy, 74, 75, 76, 95 125, 127, 128, 237 thinning, 111 United Kingdom, 127 thrombin, 28 United Nations, 27, 62, 65, 100, 101, 107, 125 thrombosis, 66 United States, 68, 103, 104, 123, 128, 237 thyroid, 19 unstable compounds, 157 time lags, 144 urbanisation, 90 time resolution, 133 urbanization, 64 tissue, 45, 48, 58, 87, 89, 92, 108, 157, 160, 186, urea, 20, 41 203, 237 urinary tract, 97 tocopherols, x, 156, 159, 160, 161, 162, 165 urinary tract infection, 97 total product, 65 urine, 69 toxic effect, x, 80, 87, 89, 156 USA, 23, 30, 41, 44, 68, 103, 105, 125, 195, 205, toxic products, 160 206, 217 toxic substances, 90, 103 USDA, 217 toxicity, 12, 14, 70, 88, 89, 90, 118, 125, 162 toxin, 87, 88, 89, 94, 95, 96, 102, 105, 125 V trace elements, 3, 191 trade, 74, 221 validation, ix, 128, 146, 149, 150 trademarks, 221 valine, 116 training, 129, 133, 135, 136, 137, 138, 139, 145, 146, valuation, 105, 106 149, 152, 216 vancomycin, 72, 99 traits, 72, 73, 119, 166, 192 variables, 132, 140, 141, 171, 172, 175, 210 transaction costs, 179 variations, 52, 129, 175 transactions, 83 varieties, 34, 35, 47, 49, 54, 55, 59, 234, 241, 245, transcription, 158 246 transcripts, 238 vector, 96, 143, 144, 149 transduction, 72, 158, 159 vegetable oil, 67, 76 transferrin, 158 vegetables, 5, 26, 93, 95, 128, 161, 167, 202 transformation, vii, 33, 35, 45, 56, 59, 72, 129 vegetation, xi, 195 transformations, 153 vertical transmission, 93 translation, 136, 137, 140, 222 vessels, 87, 130, 131 transmission, 58, 67, 75, 87, 91, 92, 93, 95, 96, 97, Vietnam, 74, 81, 83 99, 103, 104 viruses, 22, 92 transparency, xii, 139, 219 vitamin A, 14, 158, 160 transport, x, 15, 20, 22, 66, 77, 87, 90, 116, 117, 121, vitamin C, 2, 160 155, 157, 158, 161, 165, 171 vitamin E, 19, 20, 27, 158, 160, 161, 162, 165, 166, transport processes, x, 155, 158 167 transportation, 15, 20, 109, 220 vitamins, ix, 2, 66, 155, 156, 158, 166, 184, 186, transshipment, 171 187, 202 262 Index
WHO, 62, 77, 89, 90, 91, 93, 94, 101, 106, 120 W wild animals, 93 wildlife, 68, 108, 204 war, 99 windows, 136, 138 Washington, 24, 56, 77, 78, 82, 216, 217 Wisconsin, 105 waste, 69, 72, 73, 77, 172 withdrawal, viii, 61, 77, 93 wastewater, 70, 79, 81 WNN, 129, 135, 136, 137, 138, 139, 141, 142, 143, water, viii, 10, 11, 14, 20, 25, 31, 32, 35, 38, 50, 51, 145, 149, 150, 151 61, 67, 68, 70, 71, 72, 73, 74, 80, 91, 93, 95, 103, World Health Organization, 106, 120 108, 121, 131, 158, 160, 191, 215 worldwide, ix, xi, 2, 65, 72, 74, 108, 155, 195, 196, water quality, 67 203 wavelet, ix, 127, 129, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 147, 148, 149, 151, 152, 153 Y wavelet networks (WNNs), ix, 127 wavelet neural network, 129, 139, 147, 148, 151, yeast, 118, 119, 130, 131, 192 152, 153 yield, 40, 49, 151, 208, 216 web, 54, 67, 75, 101 yolk, 164, 165 weight gain, 112, 186, 189 weight loss, 20, 26, 186 Z welfare, viii, 63, 85, 86, 99, 120 zinc, 183 well-being, 109, 189 white blood cell count, 19