APPLICATIONS OF CHEMOMETRICS IN STUDYING THE INFLUENCES OF GENETIC
VARIATION AND PROCESSING ON THE CHEMICAL COMPOSITION OF FOODS
A THESIS
SUBMITTED TO THE FACULTY OF
UNIVERSITY OF MINNESOTA
BY
WESLEY MOSHER
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTER OF SCIENCE
Name of Adviser: Chi Chen
August 2020
© Wesley Mosher 2020
ACKNOWLEDGEMENTS
As I reflect on the last couple of years, I must first acknowledge an overwhelming sense of gratitude towards my advisor, Dr. Chi Chen. As I have had the opportunity to work under his guidance, his passion for scientific inquiry has been evident each day. The standard of quality he has instilled throughout the time of my studies and this thesis research will serve me well in my future career. This thesis work would not be what it is without his insights, patience, and encouragement.
I would also like to thank my lab members. It is the environment they create each and every day that has made my experience at the University of Minnesota forever memorable. Coming to the lab each day feels like spending time with family, which is a testament to the quality of friendships that have been so easily built among each of us. To
Qingqing Mao, Yuyin Zhou, Yiwei Ma, Yue Guo, and Jieyao Yuan–thank you. Also, to
Dana Yao–thank you for always being willing to share your technical expertise and experience. It is your support that makes the Chen Lab an incredibly unique experience.
Finally, to my parents–thank you for the support of your company, encouragement, and more during this time. I would not be where I am without your continual support.
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ABSTRACT
Genetic variation is an essential internal factor while processing is a common external factor that determines the chemical composition of food. Characterization of chemical changes from genetic variation and processing is a challenging but also important task in monitoring the organoleptic, nutritional, and safety properties of food.
This thesis study examined the efficacy of liquid chromatography-mass spectrometry
(LC-MS)-based chemometric analysis in defining the chemical features associated with genetic variation and processing through two case studies on wild rice and mesquite flour, respectively. Wild rice (Zizania spp.), an ancient grain revered by Native
Americans, has a more desirable nutritional profile in comparison with Asian rice.
However, the differences between the lipidome of wild rice and Asian rice has not been well examined. In this study, the LC-MS-based untargeted profiling was performed on the lipid extracts from wild rice, white rice, and brown rice. Triacylglycerols (TAG) rich in essential fatty acids α-linolenic acid (ALA) and linoleic acid (LA) and the steradienes from phytosterol dehydration emerged as the primary features that separate wild rice from Asian rice. The phytosterol content of wild rice was further analyzed through the quantification of γ-oryzanol and stigmasterol, illustrating the enrichment of these phytosterols in wild rice. The presence of steradienes in wild rice is attributed to its unique processing after harvesting. Chemical influences of processing were investigated by conducting the LC-MS-based chemometric analysis of the mesquite flour (Prosopis spp.) treated with intense-pulsed light (IPL), a novel non-thermal disinfection processing that is compatible with powdered food. Targeted analysis of acetic acid and propionic acid, two flavor compounds, showed that the influences of IPL on the concentration of
ii these two short-chain fatty acids were comparable to the effects of γ-irradiation, another non-thermal disinfection method. The untargeted analysis showed that IPL increased 9- hydroyoctadecadienoic acid (HODE), a lipid oxidation product, as well as α- ketoisovaleric acid, a degradation product of amino acids. Overall, LC-MS-based chemometric analysis proved to be a powerful tool in assessing the alterations in food due to genetic variation and processing.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... i ABSTRACT ...... ii TABLE OF CONTENTS ...... iv LIST OF TABLES ...... vi LIST OF FIGURES ...... vii CHAPTER 1: LITERATURE REVIEW ...... 1 1.1 INFLUENCES OF GENETIC VARIATION ON THE CHEMICAL COMPOSITION OF FOODS: CASE STUDY ON WILD RICE ...... 2 1.1.1 Botanic classification of wild rice...... 2 1.1.2 History and cultural implications of wild rice research ...... 3 1.1.3 Chemical composition of wild rice ...... 4 1.2 INFLUENCES OF PROCESSING ON CHEMICAL COMPOSITION OF FOODS: CASE STUDY ON INTENSE PULSED LIGHT ...... 11 1.2.1 IPL and its disinfection application on foodborne microbes ...... 11 1.2.2 Effects of non-ionizing treatments on chemicals in foods ...... 14 1.3 PRINCIPLES AND PRACTICE OF CHEMOMETRICS ...... 15 1.3.1 Introduction of chemometrics platform ...... 15 1.3.2 Applications of chemometrics in food and nutritional analysis...... 16 CHAPTER 2: CHEMOMETRIC ANALYSIS OF WILD RICE ...... 28 2.1 INTRODUCTION ...... 29 2.2 METHODS AND MATERIALS ...... 30 2.2.1 Rice samples ...... 30 2.2.2 Chemicals ...... 31 2.2.3 Sample preparation ...... 31 2.2.4 LC-MS analysis ...... 31 2.2.5 Multivariate data analysis ...... 32 2.3 RESULTS AND DISCUSSION ...... 33 2.3.1 Triacylglycerols in rice...... 33 2.3.2 Phytosterols in wild rice...... 36 2.4 CONCLUSION ...... 39 iv
CHAPTER 3: CHEMOMETRIC ANALSYIS OF INTENSE PULSED LIGHT- ELICITED CHEMICAL CHANGES IN MESQUITE FLOUR ...... 47 3.1 INTRODUCTION ...... 48 3.2 MATERIALS AND METHODS ...... 50 3.1.1 Samples and chemicals ...... 50 3.1.2 Sample preparation ...... 51 3.1.3 LC-MS Analysis ...... 52 3.1.4 Multivariate modeling and data visualization ...... 52 3.3 RESULTS AND DISCUSSION ...... 53 3.2.1 Targeted analysis ...... 53 3.2.2 Untargeted chemometric profiling ...... 53 3.4 CONCLUSION ...... 55 REFERENCES ...... 59
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LIST OF TABLES
Table 1.1. Macronutrient composition of wild rice, white rice, and brown rice...... 24
Table 1.2. Micronutrient composition of wild rice, white rice, and brown rice...... 25
Table 2.1. Composition of identified TAGs in rice...... 40
Table 2.2. Identities of detected steradienes in wild rice...... 41
Table 3.1. Chemical markers separating IPL and γ-irradiation treatments from control...... 55
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LIST OF FIGURES
Figure 1.1. Phylogenetic classification of selected staple gains...... 26
Figure 1.2. LC-MS-based chemometric analysis workflow...... 27
Figure 2.1. Representative two-dimensional chromatograms of white (A), brown (B), and wild rice (C). Representative fragmentation pattern of triglycerides from MS/MS fragmentation (D)...... 42
Figure 2.2. PCA and S-plot modeling [mass ≥ 500 m/z (+)] reveals major contribution of
TAGs in separating wild and Asian rice...... 43
Figure 2.3. PCA and S-plot modeling [mass range < 500 m/z (+)] reveals major contribution of phytosterol dehydration products in separating wild rice and Asian rice...... 44
Figure 2.4. MS/MS fragmentation and proposed fragmentation pathway for two steradienes identified in wild rice...... 45
Figure 2.5. Quantification of two detected phytosterols within extracted lipids of rice...... 46
Figure 3.1. Concentration of short-chain fatty acids in mesquite flour after IPL- and γ- irradiation treatment...... 57
Figure 3.2. Chemometric analysis of potential chemical changes in mesquite flour after
IPL- and γ-irradiation treatments...... 58
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CHAPTER 1: LITERATURE REVIEW
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1.1 INFLUENCES OF GENETIC VARIATION ON THE CHEMICAL
COMPOSITION OF FOODS: CASE STUDY ON WILD RICE
Plant and animal foods in human diets all went through either natural or artificial genetic selection, yielding a different chemical composition and nutrient profile. Wild rice, as a native grain, can serve as a good example of the influences of genetic variation on the chemical composition of foods through examining the characteristics that separate wild rice in its many forms from other staple rice products consumed throughout the world.
1.1.1 Botanic classification of wild rice
Wild rice is the grain of multiple species in the Zizania genus, including perennial species (Z. texana and Z. latifolia) and annual species (Z acquatica and Z. palustris) [1-
6]. Among them, Z. palustris is widely distributed within the Midwest region of the
United States and has historically been a staple food for Native Americans throughout the area [1-3]. Wild rice is only consumed in its whole grain form and has been recognized as a whole grain food by the USDA [5, 7]. Owing to its appealing flavor, wild rice can be consumed alone or incorporated into gourmet foods such as breakfast cereals, soups, and bakery items, and its inclusion into the standard American diet can potentially increase the consumption of whole grains [4, 5].
Wild rice shares a common family, Poaceae, with many major cereal grains
(Figure 1.1). Within Poaceae, maize, barley, and wheat belong to Pooideae subfamily, while wild rice and Asian rice (Oryza sativa) belong to Oryzoidaeae subfamily.
Compared to wild rice, Asian rice is a major staple food among two-thirds of the world’s population [8], and is also consumed in a refined form. The post-harvest refining and
2 milling processes first produce brown rice after dehulling and further produce white rice after removal of the bran and germ [9, 10]. Brown rice is a whole grain. White rice mainly contains endosperm and is not considered a whole grain. White rice is can be subjected to fortification of essential micronutrients that are lost in the milling process of removing the bran and germ [9, 10]. In the United States, white rice is fortified with iron, niacin, thiamin, and folic acid [11].
1.1.2 History and cultural implications of wild rice research
Wild rice is considered sacred to the Native American groups inhabiting the upper midwestern United States. Therefore, researchers that are interested in understanding this crop are placed in a challenging environment of balancing respect for culture and interest in furthering the body of knowledge that still contains gaps. Not all researchers are able to form relationships with tribal leaders, leaving their research program to study what is commercially available. The commercial availability of wild rice has a long history that has challenged the relationship between researchers and Native Americans.
The commoditization of wild rice as a crop in the United States was first investigated by researchers at the University of Minnesota [6, 12, 13]. In the 1980s, Ervin
A. Oelke was instrumental in producing a wild rice crop that could be cultivated in the low-lying and otherwise unprofitable lands across Minnesota [6, 12, 13]. While mechanical harvesting was attempted as early as the 1920s, it was not permitted in the
Native American reservations as they desired to retain traditional farming practices for harvesting the crop [14, 15]. Therefore, Oelke’s aim was to fundamentally alter the structure of wild rice through traditional breeding in order to be capable of withstanding new areas for growth and modern harvesting techniques. Oelke’s research provided a
3 crop resistant to common diseases and reduced shattering, which is where a ripe seed is lost to the water below prior to successful harvesting [12]. As a result of these efforts, wild rice harvesting was able to yield 300 lbs/acre in the early 1980s, which was a three- fold increase from the yield of the late 1960s [6]. Growing a successful crop is also dependent on the harvesting processes, which was another modernization opportunity.
The harvesting process of wild rice is complex. Harvesting is most successful once the wild rice grain has reached 45% moisture content in order to prevent shattering during the post-harvesting process [3]. The process through which wild rice is harvested is known as “knocking the rice,” where one harvester uses a long pole to travel through the rice bed while attempting to do as little damage as possible to the stems of the immature grain [5, 6, 13]. As the flat-bottomed canoe wades through the shallow water, a second harvester grabs an armful of the stalks, bends them over the side of the boat, and knocks the ripened wild rice grains into the canoe. Once this process is complete, the post-harvest processing begins with a fermentation step, drying of the grain, dehulling of the dried grain, and finally hull separation [5, 6, 13]. While there have been many innovations that have allowed modern agriculture to produce wild rice in greater quantities, the fundamental principles of the post-harvesting process remain unchanged
[5].
1.1.3 Chemical composition of wild rice
1.1.3.1 Macronutrients
1.1.3.1.1 Protein and amino acids
Protein content in wild rice, ranging from 12.6 to 17.6%, is higher than that of
Asian rice (Table 1.1) [16-18]. The amino acid composition of wild rice is comparable to
4 oat protein, which is known as a well-balanced cereal grain [16]. Wild rice protein is not a complete protein as it is deficient in lysine [16, 17].
The hull of the wild rice is removed prior to processing in commercial products.
Laboratory prepared wild rice samples where the hull was not removed have been compared to commercially available strains to assess the protein quality [19]. It was found that the commercial hull removal process did not affect the amino acid composition of wild rice protein as there was no difference in amino acid composition between laboratory grown and commercial samples in this respect [19].
Protein efficiency ratio (PER) allows for comparison between protein sources from different foods to a reference protein such as casein [20]. PER is determined by replacing 10% (w/w) of the standard AIN-76 feed with the protein of interest and feeding the diet to rats [21]. After a feeding period, the gram amount of weight gain of a rat is divided by the gram amount of protein consumed which yields the PER. The PER of wild rice is higher in comparison to other grains, but is overall lower than the reference protein casein [19, 22].
1.1.3.1.2 Carbohydrates
Carbohydrate content of wild rice ranges from 71 to 84% (Table 1.1) [5, 7, 17].
Wild rice has been previously investigated as a potential carbohydrate replacement in the standard American diet. One study fed four groups of mice a low fat (negative control), high fat/carbohydrate (HFC), a westernized diet similar to the standard American diet
(CD), and wild rice diet. The CD used white rice and processed wheat starch as the carbohydrate replacement, while the wild rice diet used wild rice for the carbohydrate source. After the eight-week feeding, the mice on the HFC and CD gained more weight
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(p = 0.002), had higher relative liver weights (p = <0.001), and had higher triglycerides (p
= <0.001) when compared to the low-fat and wild rice diets [23]. This result indicated that wild rice can play a protective role by modulating the events associated with metabolic disorder.
Further investigation was pursued to understand the mechanism of how wild rice increases insulin sensitivity. Gene expression experiments have demonstrated that the wild rice diet upregulated genes for encoding the adiponectin receptor, as well as PPAR-
α and PPAR-γ [23]. Decreased expression of leptin was also observed on the wild rice diet [23]. The authors concluded that it was wild rice’s effect on PPAR-α and PPAR-γ, which, “regulate the transcription of genes involved in fatty acid synthesis and insulin sensitivity” that caused the increased insulin sensitivity after consuming the wild rice diet
[23].
1.1.3.1.3 Lipids
The lipid content of Zizania spp. ranges from 0.5-1.1% [7, 24, 25], which is lower than brown rice (1.9-3.2%) and higher than white rice (0.4-0.66%) [7, 26]. Przybylski et al. (2009) studied the lipid components of North American wild rice and found that their lipid content is defined by a major contribution from linoleic acid and α-linolenic acid, with other minor contributions by oleic acid and palmitic acid [25].
The omega-3 fatty acid content of wild rice is relatively high among grains. The omega-6:omega-3 ratio serves as a quality indicator of dietary fatty acids since a lipid intake with the ratio close to one is generally considered as beneficial for human health
[27, 28]. An analysis of seven selected wild rice samples showed that their omega-
6
6:omega-3 ratios ranged from 1.1:1-1.8:1, which is advantageous compared to the standard American diet’s ratio of over 15:1 [25, 27].
1.1.3.1.4 Fiber
The total dietary fiber content in wild rice is reported in the U.S. Department of
Agriculture food database at 6.2% [26]. The consumption of fiber is important for maintaining digestive system functions and is currently recommended to be consumed at a level of 25 g/d [29]. In order to be considered a “good source of fiber,” a food must contain at least 10% of the recommendation of fiber per serving. Wild rice can be considered a good source of fiber, as it contains more than 10% of the recommendation of fiber per 45 g serving.
1.1.3.2 Micronutrients
1.1.3.2.1 Vitamins
Thiamin (vitamin B1) is a water-soluble vitamin present in most whole grains.
Thiamin is the precursor of thiamine pyrophosphate, a coenzyme of the catabolic enzymes in glucose and amino acid metabolism, such as pyruvate dehydrogenase and branched-chain amino acid dehydrogenase [30]. In comparison to enriched white rice and brown rice, wild rice has a lower level of thiamin (Table 1.2) [5]. The recommended dietary allowance (RDA) for thiamin is 0.5 mg/100 kcal. Therefore, a 45 g serving of wild rice provides approximately 9% of the RDA.
Riboflavin (vitamin B2) is essential for energy metabolism. Riboflavin is the precursor of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), coenzymes that use their reversibly oxidized and reduced forms to conduct electron and energy transfer [31]. The RDA of riboflavin is 1.3 mg/d for adult males and 1.1 mg/d for
7 adult females [32]. The concentration of riboflavin in wild rice is almost five times that of white rice and 2.5 times that of brown rice [26]. A typical 45 g serving of wild rice can provide approximately 10% of the RDA of riboflavin.
Niacin (vitamin B3) is the common term for nicotinamide and nicotinic acid, which are the precursors of NAD+, NADH, NADP+ and NADPH, the essential coenzymes in redox reactions, electron transfer, and energy metabolism [32]. The niacin requirement can be met through the diet or from the conversion of tryptophan [32, 33].
The RDA for men and women are 16 and 14 mg/d, respectively [32]. The concentration of niacin in wild rice is similar to that of brown rice and much higher than that of white rice [26]. A typical 45 g serving of wild rice can provide approximately 17% of the RDA of niacin.
Vitamin E is a class of lipid-soluble antioxidants, including four tocopherols and four tocotrienols [34]. Among them, α-tocopherol has the highest antioxidant capability
[34]. In humans and animals, vitamin E mainly distributes in the membrane and functions as an electron acceptor to break the chain of radical formation and propagation, preventing the peroxidation of polyunsaturated lipids and fatty acids [35]. In plants, vitamin E also protects the photosynthesis apparatus as well as prevents the autoxidation of lipids during storage [36]. The concentration of vitamin E in wild rice is eight times higher than that of white rice and 1.3 times higher than that of brown rice [26]. The RDA of vitamin E for adults is 15 mg/d. Therefore, a typical 45 g serving of wild rice can provide approximately 2% of the RDA [37]. As for other vitamins, wild rice is not a better source of vitamin A or vitamin C in comparison to white and brown rice [2, 5].
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1.1.3.2.2 Minerals
Wild rice has markedly different mineral content compared to brown and white rice (Table 1.2) [26]. Wild rice contains a notably higher level of zinc and potassium in comparison to white and brown rice [26]. Its zinc content is also higher than many other grains such as oats, wheat, and corn [5]. In addition, wild rice is rich in calcium, iron, magnesium, and phosphorus [26].
1.1.3.3 Non-nutrient bioactives
1.1.3.3.1 Phytosterols
Phytosterols are cholesterol analogs in plants. The major phytosterols, such as β- sitosterol, stigmasterol, campesterol, sitostanol, and campestanol, are known for their functions of interfering the absorption of dietary cholesterol and reducing the blood levels of low-density lipoproteins (LDL) [38, 39]. The activity of phytosterols in controlling serum LDL and cholesterol is generally considered beneficial for human health because cholesterol is strongly associated with adverse cardiovascular outcomes [38]. Phytosterol content in wild rice has been previously studied [25]. Wild rice contains a very large amount of phytosterol, ranging from 70-145 g phytosterol/kg lipid [25]. This represents a
3.5 times higher level than other cereal by-products [25, 40]. The main phytosterols in wild rice are campesterol, β-sitosterol, and cycloartenol [25]. Collectively, these phytosterols contributed to 54-75% of the total sterol content within seven samples of
Zizania palustris [25].
Potential anti-atherogenic effects of phytosterol content in wild rice have been observed in LDL-receptor knockout (LDL-r-KO) mice by replacing dietary carbohydrate content with wild rice [39]. After correction for the total lipid content, wild rice yields
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0.03-0.06 g of phytosterol per 45 g serving [4, 25]. Even if consuming wild rice twice per day, this phytosterol content alone is insufficient to lower LDL cholesterol in humans. In order to observe the maximum beneficial effect of phytosterol consumption in humans, studies with human subjects indicate that intake should be 2 g phytosterol per day [41]. In order to meet the requirement for 2 g of phytosterol exclusively from wild rice, 1.5-3.0 kg/day would need to be consumed.
1.1.3.3.2 Arsenic
Arsenic contamination is a public health issue that is worldwide and is complicated by a lack of governmental control and heavy reliance on rice as a staple grain in developing countries. The World Health Organization considers arsenic one of the ten chemicals of major public concern [42]. Further, “millions of people around the world are exposed to arsenic at concentrations much higher than the guideline value,” which is 10 µg/L [42]. Data from the China National Nutrition and Health Survey estimated that the consumption of inorganic arsenic averages 24.5 µg/d [43]. Arsenic consumption in children in the United States has been estimated at a much lower level of
3.2 µg/d [44].
Food that is farmed using water containing inorganic arsenic causes toxicity to a variety of organs and bodily systems [45]. Arsenic has been extensively studied as a contaminant in brown rice and white rice. Arsenic content in brown rice has been reported at 154 ppb inorganic arsenic, while white rice contains approximately one-third less at 92 ppb [46]. However, the FDA has noted that the level of contamination belies the true risk, as white rice is consumed more frequently than brown rice.
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While the body of evidence indicating Asian rice contamination is extensive, less is known about wild rice’s potential to contain arsenic. Rice sampling processed by analytical techniques indicate that brown rice contains an average of 7.2 µg/serving from
99 samples analyzed [47]. The data for wild rice is convoluted by the choice to include carnaroli and “mixed types;” however, the figure is still lower in average in comparison to brown rice at 5.6 µg/serving from a total of 6 samples analyzed [47]. Another study specifically analyzed wild rice found in northern Wisconsin and reported arsenic concentrations of 0.11 ppm [48].
1.2 INFLUENCES OF PROCESSING ON CHEMICAL COMPOSITION OF
FOODS: CASE STUDY ON INTENSE PULSED LIGHT
Processing is an indispensable component of modern food manufacturing and preparation that commonly aims to improve the function and safety of foods. However, physical and chemical treatments in food processing are expected to alter the chemical composition of foods. In this thesis research, intense pulsed light (IPL), a novel disinfection technology, serves as an example for examining the influences of processing on the chemical composition of foods.
1.2.1 IPL and its disinfection application on foodborne microbes
Pathogenic foodborne microbes are a major concern in public health and food safety around the world. According to the Centers for Disease Control and Prevention
(CDC), foodborne bacterial infections cause millions of illnesses and thousands of deaths each year in the United States [49]. The sources of microbial contamination in food production are diverse, including irrigation water, manure, and field sanitation [49, 50].
Because of the inevitability of contamination, post-production and pre-distribution
11 disinfection needs to be conducted to minimize exposure of consumers to pathogenic microorganisms.
Disinfection processes can be generally categorized as thermal processing and non-thermal processing [51]. These processing platforms increase the safety of the final product by reducing harmful microorganisms such as Salmonella, Escherichia coli (E. coli) O157:H7, Listeria monocytogenes (L. monocytogenes) and Campylobacter jejuni
[52]. In thermal disinfection, the food matrix is subjected to temperatures between 60 °C and 100 °C. The thermal energy in the processing can affect the sensory and nutritional properties of final food products [53]. Non-thermal disinfection, in theory, can eliminate microorganisms while avoiding the heat-elicited damage to the sensory and nutritional properties of food [51, 53]. Radiation is a common non-thermal disinfection platform.
Food disinfection processes can be either ionizing or non-ionizing. Ionizing radiation is a common non-thermal process [53]. Ionizing radiation uses either X-rays, electron beams, and gamma rays of radioisotopes 60Co and 137Cs [53, 54]. This process is useful in disinfecting grains, legumes, spices, and fruits. However, its application for high-protein foods, such as milk, has challenges in maintaining sensory properties, as protein degradation in the treatment can result in undesirable flavors and colors [53].
Non-ionizing food processing methods include ultra-high-pressure processing
(HPP), ultraviolet (UV) light, pulsed electric fields (PEF), and intense pulsed light (IPL)
[55]. Continuous UV from 100 to 400 nm wavelengths is a common food processing technique due to its disinfection efficacy [56]. The UV spectrum is divided into three regions: UVC, or short-wave (200 to 280 nm); UVB, or medium-wave (280 to 320 nm); and UVA, or long-wave (320 to 400 nm) [57]. The UVC range is considered to be the
12 most lethal to microorganisms, specifically at the 254 nm wavelength [51], because it affects the pyrimidine structure of deoxyribonucleic acid (DNA) by forming cyclobutene pyrimidine dimers, causing double strand DNA breaks and DNA-protein cross-links [57-
59]. These UV-elicited lesions inhibit ribonucleic acid polymerase and ultimately prevent
DNA replication, which are required for cell maintenance and proliferation of microorganisms [58].
IPL is a form of non-ionizing radiation that uses a broad-spectrum light of approximately 180 to 1100 nm in wavelength [51]. The mechanism underlying the bactericidal activity of IPL is still under investigation. The UV portion of IPL is expected to a major contributor to its efficacy [60]. By using a full spectrum and delivering bursts of light that have higher peak power than continuous UV, IPL technology inactivates microorganisms more effectively than continuous UV [61, 62].
The potential uses of IPL technology to safely and efficiently disinfect different food matrices have been investigated. The study on the influence of IPL on yeast (S. cerevisiae) showed structural changes in the yeast cells after treatment [61]. Transmission electron microscopy images after three pulses of the IPL system indicated that S. cerevisiae had damage to 50% of the cells, resulting in vacuole expansion and cell membrane distortion [61]. This effect of IPL was not observed in the continuous UV treatment, indicating the advantage of a full spectrum light source over only UV wavelengths.
Effects of IPL and UVC on two pathogenic foodborne bacteria, L. monocytogenes and E. coli were also compared [51]. The treatment time of UVC radiation was 20 min, which caused 4-log and 5-log reduction in the microbial activity of L. monocytogenes and
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E. coli, respectively [51]. The IPL treatment was able to achieve 7-log reduction after a
90 s treatment in L. monocytogenes and 60 s treatment in E. coli [51]. Other foodborne pathogens, such as Salmonella enteritidis, Bacillus cereus, and Aspergillus niger, have had their microbial activity reduced as a result of treatment with IPL technology [63].
1.2.2 Effects of non-ionizing treatments on chemicals in foods
Non-ionizing processing causes changes to the food matrix that may result in undesirable sensory and quality changes. Ultra-high pressure processing (HPP) uses pressure up to 6000 times atmospheric pressure to inactivate the non-covalent bonds in the membrane of microbial cells, but it could also cause damage to foods [53]. The effects of HPP on onions has been previously investigated. After exposure to HPP, a sensory panel judged the onions to “smell less intensely and rather like cooked or fried onions” [64]. Gas chromatography-mass spectrometry (GC-MS) headspace analysis of the onions was performed to correlate with the sensory data. After HPP, dipropyldisulfide, a compound that imparts the characteristic odor of onion, was strongly decreased, while compounds responsible for the fried onion odor, transpropenyldisulfide and 3,4-dimethylthiophene, were increased [64].
Other non-ionizing processing techniques could also alter the chemical profile of foods. High intensity pulsed electric fields (PEF) use a capacitor and high-voltage power supply to discharge electricity in bursts in the sub-microsecond to millisecond range [53].
This technology is best applied to a liquid food matrix. PEF can achieve comparable microbial inactivation as heat pasteurization while retaining the sensory attributes of the food [65]. Chemometric analysis of fruit juice and soymilk beverages treated with PEF has been conducted to determine the influences on phenolic and carotenoid contents [65].
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After treatment, total phenolic concentration was significantly higher in the PEF treated samples compared to the thermally treated controls [65].
Because IPL is still an emerging non-ionizing process, limited information is available about its effects on the chemicals within foods. However, as other non-thermal technologies can cause changes to the chemical profile of the food matrix, the extent of
IPL’s influence on food remains to be investigated. The amino acid analysis of non-fat dry milk has previously revealed that no significant changes occurred to the amino acid profile of IPL-treated samples compared to the control [66]. Peptides were observed in
IPL-treated samples that originated from the c-terminal sequence of milk’s casein protein, but the nutritional relevance of these changes were identified as negligible since they do not affect the amino acid composition [66].
1.3 PRINCIPLES AND PRACTICE OF CHEMOMETRICS
Chemical changes from genetic variation and processing can occur to many components in foods. Traditional targeted chemical analysis is only able to detect specific chemical changes but lacks the capacity to detect unknown chemical changes in foods.
Chemometrics has been an effective tool to detect and monitor the chemical changes in the complex chemical matrix of foods.
1.3.1 Introduction of chemometrics platform
Chemometrics examines the chemicals in complex matrices using advanced chemical analysis, modeling, and informatic tools. Metabolomics is an important branch of chemometrics used by systems biology researchers to monitor the perturbations to a biological system over time [67]. Measurement of the small molecules within biological samples (blood, urine, feces) is the backbone of metabolomic analysis [68]. Paired
15 together with the complementary analysis of genomics, transcriptomics, and proteomics, metabolomics plays an important role in forming a complete picture for a researcher when defining a biological system [67]. An advantage of metabolomic analysis is that the metabolites it measures are the closest determinant of the ultimate species’ phenotype
[67]. Further, there is a relatively smaller number of metabolites in comparison to genes, transcripts, and proteins [68].
Within food science and nutrition, chemometrics is a powerful tool that can be used to assess the chemical composition of foods. A typical chemometrics workflow begins with sample processing and preparation. After the samples are processed and prepared, they are analyzed using an analytical platform, such as mass spectrometry or nuclear magnetic resonance. After the data are collected, it is processed using multivariate statistical approaches that aim to maximize the data’s potential for answering the questions that were being investigated [69]. The following subsections provide additional detail on this workflow (Figure 1.3).
1.3.2 Applications of chemometrics in food and nutritional analysis
Understanding the unique chemical composition of individual foods has many applications in assessing food safety and ensuring product quality. Chemometrics is an important tool in these efforts. Chemometric analysis provides useful information on the nutrient profile and organoleptic properties of foods, which can facilitate their enhancement and preservation. Therefore, the application of chemometrics in foods has been called “foodomics”, which as defined as “a discipline that studies the food and nutrition domains through the application of omics technologies” [70].
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Another application of chemometric analysis is for dietary intake evaluation.
Human nutrition researchers have struggled with the accuracy of nutritional surveys due to the limitations of traditional assessment methods. For example, food frequency questionnaires are a routine choice for studying food intake. Bias arises in the food frequency questionnaire due to the nature of the questionnaire, as social pressure to follow an “ideal” diet may overcome truthful disclosure of intake [71]. In order to overcome this drawback, food frequency questionnaires can be validated, a process by which uses a reference instrument to compare intake against weighed portion sizes [71].
A drawback to validating food frequency questionnaires is the amount of work that is required to do so. Other methods such as 24-h dietary recalls, dietary records, and dietary history also exist, but each method has its respective challenges, leaving the nutrition researcher to balance the drawbacks of each method before settling on one method.
However, one such drawback of all of these methods is the prevalence of reporting bias that disproportionately affects reported protein intake [71]. Chemometric analysis of foods represents an opportunity to address these issues by seeking to understand the foundational differences in foods and providing context for understanding on how foods can potentially impact dietary patterns and outcomes.
1.3.2.1 Sample preparation and LC-MS analysis
Sample preparation methodology is chosen based on a variety of factors. In order to be compatible with the liquid nature of the LC-MS platform, food items are processed with a variety of liquid extraction methods. Two extraction methods that are routinely used for separating lipids from hydrophilic components in foods are the Bligh and Dyer method [72] and the Folch method [73]. Both methods use methanol and chloroform as
17 the extraction agents. Comparison of the two methods indicates that at a lipid content of
<2%, both methods are similar in their extraction yield. However, once samples are analyzed with a lipid content >2%, the Bligh and Dyer method was not able to capture the full lipid content of the samples and estimates of lipid content were measured at up to
50% lower [74]. Both of these lipid extraction methods have been used extensively in lipid analysis.
Analysis of short-chain carboxylic acids, aldehydes, and ketones has been previously explored [75]. These metabolites are of significant interest to the nutritional researcher due to their prevalence within biological processes. Due to the nature of the
LC-MS system, there are still challenges in chromatographic performance due to low ionization efficiency when analyzing metabolites that contain these functional groups
[75]. In order to address this issue, a variety of chemical derivatization options can be considered during the sample preparation. One method uses 2,2’-dipyridyl disulfide and triphenylphosphine, which react with the metabolites of interest to form acyloxyphosphonium ions. 2-Hydrazinoquinoline further reacts to form hydrazones
(aldehyde and ketone reaction products) and hydrazides (carboxylic acid reaction products) [75]. This derivatization step during sample preparation allows for enhanced chromatographic separation and detection capability.
Amino acid composition of food is necessary to understand the potential for foods as complete proteins as well as analysis such as determining the limiting amino acids within the food. A manner in which to prepare samples for processing in the LC-MS system was originally described in Tapuhi et al. (1981) [76]. Dansyl chloride reacts with the amino group of amino acids to form dansylated amino acids and hydrochloric acid.
18
Temperature and pH of the reaction must be considered in order to form the desired products, otherwise alternative and less desirable reactions can occur [76]. Further advances in the LC-MS system, such as chromatography columns with sub-2 µm particle have led to further refinement of the method [77]. Once the samples have been processed, they are ready for analysis on the LC-MS system.
There are many factors that must be considered when preparing for analysis of food samples on the LC-MS system after sample preparation. Mobile phases must be selected and optimized to ensure the analyte of interest is retained within the column and eluted into the mass spectrometer in a sequence to provide the most robust sensitivity and resolution. Solvent selection is based on the polar interactions of which a solvent is capable: acidic, basic, or dipolar [78]. Solvent strength and optimization is based on the ability of the chosen solvent to effectively, “provide convenient sample retention and an even spacing of bands within the chromatogram” [78]. Other factors that can affect mobile phases which must be considered are pH and the elution gradient [79, 80].
Overall, solvent choice represents only one of many factors that must be considered for successful LC-MS analysis.
Another consideration in LC-MS analysis is the column selection, which is guided by the analyte of interest as no one column is able to cover all potential analytes.
Reverse-phase high pressure liquid chromatography utilizes a nonpolar stationary phase as the column material, typically a silica that is boded to an alkyl chain of eight or eighteen carbons, which are examples of columns used in reverse-phase LC. As the alkyl sidechain length increases, so too does the hydrophobicity of the stationary phase [81].
Solvent choice and column selection are important factors in consideration when
19 optimizing the LC for successful analysis. Other considerations include column temperature and flow rate of the mobile phases. After chromatographic separation, the eluate is transferred to the mass spectrometer.
The mass spectrometer is also a complicated piece of analytical equipment. While there are also many considerations that emerge when optimizing the MS, ionization source and mass analyzer are two major considerations. Introduction of the eluate from the LC is done so by an ionization source. There are multiple ionization methods available, including atmospheric-pressure chemical ionization (APCI), electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) [82]. ESI is considered a “soft” ionization technique, as it functions to charge the incoming eluate while not causing excessive fragmentation of the parent ion [82]. An advantage of ESI is that the parent ion is minimally disturbed, which is beneficial when working with samples that have low concentrations. Further, ESI can cover a broad range of analyte polarities as well as a large molecular weight range. Using both positive and negatively-charge analytes during MS analysis provides a more comprehensive coverage of potential chemometric features than choosing one polarity alone. Desolvation, or removal of the mobile phase from the analytes, must occur in tandem with the ionization. These steps are required to prepare the analytes for mass analysis, as mass analyzers require a charge in order to transport the analytes to the mass detector.
There are many types of mass analyzers available, such as quadrupole, ion-trap, orbitrap, and time-of-flight. Due to the high resolution and sensitivity of quadrupole time of flight (Q-TOF) instruments, they are most suitable for chemometric analysis [83]. Q-
TOF-MS instruments contain the benefit of two mass analyzers: quadrupole and time-of-
20 flight [84]. After ionization, the ions are channeled into the first quadrupole, which acts as a mass filter and selects ions within the desired range [84]. Depending on the type of analysis, the second quadrupole can introduce collision gasses such as argon or nitrogen, which results in the parent ion’s fragmentation in a process called collision induced dissociation [84]. Once the ions pass through the second quadrupole, they are introduced into the time-of-flight, which uses an ion pulser to orthogonally push the ions towards a reflectron and subsequently back towards the ion detector in a parabolic path [84]. Ions that have a lighter mass will more quickly move through the time-of-flight, while ions with heavier mass will move more slowly [84].
1.3.2.2 Data processing and analysis
One of the challenges that chemometric analysis faces is the amount of data that is generated during the analysis. Therefore, an established method in order to transform the data from the raw file into a format that can easily be analyzed was necessitated [85]. A typical chemometrics workflow begins with experimental design, data collection, data pre-processing, data analysis, and data interpretation [86]. During data pre-processing and analysis, the investigator must assess the signals of different analytes and determine an identity of the analytes in the sample [86]. As in the other -omics strategies, the data that is generated will be filled with information that will not be helpful to the researcher in creating novel insights. As a result, it is incumbent upon the investigator to extract the most meaningful portions of the data from the information [87]. Considering that only
2.5% of metabolites have known chemical standards, the availability of commercial databases to match compounds based on accurate mass, such as METLIN, is needed [88].
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There are many steps in reducing the complexity of the data generated from the
LC-MS. Data must first be pre-processed by “noise filtering and baseline correction, peak detection and deconvolution, alignment, and normalization” [89]. Noise filtering seeks to remove the excess signal that is not of interest, specifically of the solvents used during sample prep and the mobile phases of the LC system [89]. Peak detection functions to identify and quantify the chemometric features that define the peaks of the chromatogram
[89]. The LC-MS system is subject to retention time “drift,” where the peaks associated with the same analytes will increasingly elute later over the course of an extended run of samples. This phenomenon is addressed by alignment of the spectral data [89]. Finally, normalization of the data seeks to increase the importance of the low-abundant analytes while not increasing the noise, for which Pareto scaling is well suited [89]. After the pre- processing steps, the data is ready for multivariate modeling analysis.
Data visualization through modeling allows for reduction of the complexity of the data while retaining information that is relevant and valuable to assist in answering the research question. Due to the number of samples and variables within each sample, data collected in chemometric studies is typically considered multivariate. In order to assess multivariate data, either unsupervised or supervised techniques can be used. Principal components analysis is an unsupervised method that is heavily relied upon in the field of chemometrics, as well as clustering analysis [90]. In a scenario where the classes of data are known to the researcher, a supervised model can be used to guide the analysis. Partial least squares-discriminant analysis is a multivariate data analysis tool that allows for the relationship between the dependent and independent variables to be made a factor during
22 the data processing. These techniques build on one another and are ubiquitous in chemometrics analysis [91].
1.3.2.3 Structural elucidation via MS/MS
During the ionization process, ions are created which result in the addition of a
+ + + hydrogen [M+H] , or adduct ions such as or [M+NH4] and [M+Na] [92]. The nitrogen rule assists in the determination of the number of nitrogen atoms, depending on the adduct [92]. For example, an odd number of nitrogen atoms in the parent compound will cause the m/z to be odd, while an even number of nitrogen atoms in the parent compound will cause the m/z to be even [92]. Fragmentation patterns differ between even and odd numbered molecules. Even-electron ions are subject to the parity or even-electron rule – essentially, an even electron fragment ion is more likely than an odd numbered fragment ion [92].
TAG structural elucidation through use of ESI-MS-MS has been previously studied [93]. Fragmentation of lipids typically occurs at the sn-1 and sn-3 positions rather than the sn-2 position [93]. After separation by the LC, TAGs are not able to be ionized as they are neutral species. Therefore, ammonium acetate is added to the LC liquid phase solvents to increase the ability of the mass detector to register a signal. After exiting the
ESI, the mass detector filters out ions that are not of interest, from which the parent ion proceeds to a collision chamber that is filled with an inert gas. Collision-induced dissociation causes loss of a single fatty acid (FA), rendering a neutrally charged diglyceride for comparison of each unique FA loss, termed “neutral loss” [93, 94].
Comparison between the neutral loss and the parent ion leads to insight as to the identity of the individual fatty acids attached to the glycerol backbone.
23
Table 1.1 Macronutrient composition of wild rice, white rice, and brown rice
Macronutrient Wild Rice: raw White Rice: long Brown Rice: long grain, enriched, raw grain, raw [26] [26] Energy (kcal/100 g) 354 [26] 365 367 Protein (g/100 g) 12.6-17.6 [16- 6.67 7.54 18] Lipid (g/100 g) 0.5-1.1 [7, 24, 0.66 3.2 25] Carbohydrate (g/100 g) 71-84 [17, 26] 79.95 76.25 Dietary fiber (g/100 g) 6.2 [26] 1.3 3.6
24
Table 1.2 Micronutrient composition of wild rice, white rice, and brown rice [26]
Micronutrient (mg/100 Wild Rice: raw White Rice: long Brown Rice: long g) grain, enriched, raw grain, raw Calcium 21 28 9 Iron 1.96 4.31 1.29 Magnesium 177 25 116 Phosphorus 433 115 311 Potassium 427 115 250 Zinc 5.96 1.09 2.13 Thiamin 0.115 0.576 0.541 Riboflavin 0.262 0.049 0.095 Niacin 6.733 4.192 6.494 Vitamin E 0.82 0.11 0.60
25
Figure 1.1: Phylogenetic classification of selected staple grains.
26
Figure 1.2: LC-MS-based chemometric analysis workflow.
27
CHAPTER 2: CHEMOMETRIC ANALYSIS OF WILD RICE
28
2.1 INTRODUCTION
Wild rice (Zizania spp.), known for its rich flavor and hearty texture, is consumed widely as a whole grain product in the United States. Wild rice has been grown in the
Midwest and was originally cultivated as a staple grain in the diet of Native Americans in that area. While there has been much investigation in to the nutritional and health promoting aspects of wild rice, there are still gaps in the understanding that can be addressed. To date, the most comprehensive investigation into the lipid content of wild rice was performed by Przybylski et al. (2009) [25]. Therefore, the goal of this research was to perform an untargeted chemometric analysis on the lipidome for the purpose of investigating the triacylglycerol (TAG) and phytosterol content in white rice (Oryza spp.), brown rice (Oryza spp.) and wild rice.
Wild rice has a desirable nutritional profile, due to its high level of protein, fiber, and diverse lipid content [3, 7]. Przybylski et al. (2009) measured the concentration of lipids in wild rice between 0.7% and 1.1% [25]. In comparison to brown rice’s lipid content of 2.7%, wild rice is lower in overall lipid content [25]. Palmitic acid levels are similar between brown and wild rice, yet there is a two-fold increase in oleic acid in brown rice in comparison to wild rice. However, wild rice has a concentration eleven to eighteen times greater of linolenic acid, a polyunsaturated fatty acid [25]. Increasing unsaturation of fatty acids leads to a decrease in their melting points. This could be beneficial to plants such as wild rice that are grown in areas that experience temperatures near freezing. Overall, wild rice has excellent levels of the B vitamins thiamin, riboflavin, and niacin, as well as minerals calcium, iron, magnesium, potassium, phosphorus, and zinc [5, 7].
29
The health benefits of consuming wild rice have been previously investigated through animal studies. Incorporation of wild rice into the diets of mice fed 0.06% (w/w) cholesterol-containing feed with 60% (w/w) of the carbohydrate content in the feed replaced by wild rice caused a significant decrease in the size of atherosclerotic lesions after twenty weeks [39]. The LDL-r-KO mice were also fed a diet that supplemented phytosterols in the wild rice feed. After the feeding period, the severity of atherosclerotic plaque buildup was decreased compared to control diet [39]. A follow-up study to assess the mechanism of this observation led researchers to determine that wild rice consumption reduced monocyte adhesion in the aorta of the LDL-r-KO mice [95].
There is limited investigation on the chemical and nutrient composition of wild rice, especially its lipid content. In the current study, LC-MS-based lipidomic analysis was applied to commercially available white, brown and wild rice samples.
2.2 METHODS AND MATERIALS
2.2.1 Rice samples
White, brown, and wild rice samples were collected from grocery stores throughout the Minneapolis/St. Paul area. Three white and three brown rice brands were purchased from Market Pantry (Target Corp., Minneapolis, MN), Everyday Essential
(Supervalu Inc., Eden Prairie, MN), and Riceland (Riceland Foods, Stuttgart, AR), respectively. Six wild rice brands were purchased from Lundberg (Lundberg Family
Farms, Richvale CA), Now Real Food (NOW FOODS, Bloomingdale, IL), Quality Wild
Rice (Quality Rice Products, Inc. Garrison, MN), Fall River Wild Rice (Fall River Wild
Rice, Fall River Mills, CA), Gourmet House (Riviana Foods Inc., Clearbrook, MN), and
Minnesota Cultivated Wild Rice (Red Lake Nation Foods, Red Lake, MN), respectively.
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2.2.2 Chemicals
LC-MS-grade acetonitrile was purchased from Fisher Scientific (Houston, TX);
LC-MS-grade methanol from Avantor Performance Materials (Radnor, PA); LC-MS- grade butanol from J.T. Baker Chemical Co. (Phillipsburg, NJ); hexane from Fischer
(Houston, TX); ethyl acetate from Mallinckrodt Chemicals (St. Louis, MO); chemical standards γ-oryzanol and α-tocopherol from Sigma Aldrich (St. Louis, MO); β-sitosterol from US Biochemical Corp. (Cleveland, OH); and stigmasterol from TCI (Portland, OR).
2.2.3 Sample preparation
Wild rice was ground in a consumer-grade coffee mill and filtered through a #35
(USA standard) mesh sieve, stored in 1.5 mL vials, and placed into a -20 °C freezer.
Then, 50 mg aliquots of ground wild rice were weighed in 1.5 mL vials, to which 500 µL of hexane was added. The vial was briefly vortexed, sonicated for 5 min, and centrifuged at 21,000 × g for 10 min. The supernatant was transferred to a 1.5 mL vial, and the extraction process was repeated with ethyl acetate. The combined hexane/ethyl acetate fractions were vortexed and then dried in nitrogen gas until all solvent had been evaporated. The remaining powder was reconstituted in butanol, vortexed, and stored in a
-20 °C freezer until further analysis could be completed.
2.2.4 LC-MS analysis
The analysis was performed by a Waters Acquity ultra-performance chromatography (UPLC) system (Milford, MA). A 5 µL aliquot of the lipid extraction solution was injected into the UPLC and separated in a BEH C8 column with a gradient of mobile phase. The mobile phases (60:40 water:acetonitrile, 10 mM NH4 formate, 0.1% formic acid and methanol, 10 mM NH4OAc, 0.1% formic acid) were introduced into the
31 column with a gradient as follows [minute (ratio)]: 0-0.5 (55:45), 2.5-5 (20:80), 5-8
(15:85), 8-8.1 (5:95), 8.1-9.1 (0:100), 9.1-10 (55:45). The LC eluate was directly introduced into a Waters QTOF mass spectrometer for accurate mass measurement.
Capillary voltage and cone voltage for electrospray ionization (ESI) was maintained at 5 kV and 40 V for positive-mode detection, respectively. Nitrogen was used as both the cone gas (50 L/h) and desolvation gas (800 L/h) and argon was used as collision gas. The mass spectrometer was calibrated for the determination of accurate mass using a sodium formate solution (m/z range 50-1200) and monitored by injection of lock mass reserpine
([M+H]+ = m/z 609.2812). The mass chromatograms and spectral data were acquired and processed by MassLynxTM software (Waters) in centroided format. Additional structural information was obtained by tandem MS (MS/MS) fragmentation with collision energies ranging from 10 to 50 eV.
2.2.5 Multivariate data analysis
Chromatographic and spectral data were analyzed using MarkerLynxTM software
(Waters). The data was then imported into SIMCA-P+ software (UMetrics, Kinnelon,
NJ). Principal components analysis (PCA) and orthogonal projections to latent structures- discriminant analysis (OPLS-DA) models were generated after data were transformed by mean-centering and Pareto optimization to analyze the data from all three classes of rice.
Potential chemometric markers were determined by analyzing ions contributing to the principal components and to the separation of sample groups in the loading and scatter plots. The marker structures were identified by accurate mass measurement coupled with database search (Metlin, https://metlin.scripps.edu), MS/MS fragmentation, and authentic chemical standard, when available.
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2.3 RESULTS AND DISCUSSION
2.3.1 Triacylglycerols in rice.
The triacylglycerol (TAG) content of wild, white, and brown rice samples was analyzed by LC-MS. A representative two-dimensional chromatogram [96] of wild rice illustrates the distribution and qualitative abundance of each TAG (Figure 2.1 A, B, and
C). Qualitative analysis using two-dimensional chromatograms revealed additional TAGs present in the chromatogram that were not originally able to be visualized in traditional total ion chromatogram. Two-dimensional chromatographic data revealed the inclusion of both ammonium and sodium adducts within the figure, which are illustrated through multiple bands at the point of each TAG. White rice had fourteen major TAGs present in the two-dimensional chromatogram—both brown rice and wild rice contained sixteen
TAGs. After these major features were identified, structural information was determined using MS/MS spectral data (Figure 2.1 D).
There has previously been investigation into the lipid content of wild rice [7, 25].
Typical study of lipids requires the destruction of the TAG structure which allows for analysis of the fatty acid methyl esters. An advantage of the LC-MS sample preparation is the preservation of the TAG structure, which revealed insight into the distribution of fatty acids within the TAG. Saturated fatty acids are commonly found in sn-1 and sn-3 positions within the TAG, which can influence dietary absorption [97, 98]. Stereospecific determination of fatty acid structure within wild rice TAGs was not performed in this study. Therefore, future research can investigate this question to provide a more complete profiling of the wild rice lipidome.
33
Chemometrics using the LC-MS creates large datasets which must be analyzed through modeling. One such model, the principal components analysis (PCA) model, does so through reducing the dimensionality of the data so that the features within the data that most explain their respective differences are plotted along a linear component
[99]. A PCA model is a combination of two separate linear components which explain both the primary and secondary variance within the samples.
Within the rice samples, separation between the three rice varieties occurs along both principal components (Figure 2.2 A). The majority of separation occurs between the two different botanic classifications, which is demonstrated by the separation in component one. Within the Oryza spp., separation is mainly observed within component two. The separation along component one represents the lipidomic features within
Zizaniza spp. that differentiate those samples from Oryza spp.
Orthogonal partial least squares discriminant analysis (OPLS-DA) is another modeling tool used to interpret -omics data. As illustrated in the S-plot (Figure 2.2 B) that is generated from the OPLS-DA model, high-abundance TAGs in wild rice V, VI,
XII, and XIII are largely responsible for shaping the differences between botanic varieties. These four TAGs all contain either α-linolenic acid (ALA) or linoleic acid
(LA).
Lipidomic profiling of Oryza sativa through LC-MS-based lipidomic analysis has previously indicated that this species of rice has a combined 97.9% of fatty acids from carbon chain lengths of sixteen and eighteen [100]. This analysis was performed using rice bran oil, which is extracted after the polishing phase in rice production when the bran layer has been separated from the endosperm. The analysis performed in the current study
34 indicate that the majority of fatty acids in TAGs within Oryza sativa are of sixteen and eighteen carbon chain length (Figure 2.2 C).
Consumption of essential fatty acids is beneficial in the prevention and management of diseases such as coronary heart disease and diabetes [101]. ALA and LA are two essential polyunsaturated fatty acids (PUFA) which are the dietary precursors for long-chain polyunsaturated fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) [102]. These PUFAs are required for successful growth and development during the formative years, as well as maintenance of the nervous system and brain health throughout life [102, 103].
A heatmap of all TAGs that were confirmed using MS/MS structural identification is presented in Figure 2.2 C. Based on the heatmap analysis, TAGs containing ALA and LA were the most prevalent in differentiating the wild rice samples from white and brown rice. In comparison, white and brown rice samples were defined by TAGs that contained palmitic and oleic acid. These insights are supported through previous investigation on the wild rice lipidome [25].
Interestingly, a comparison between the distributors of the wild rice samples that were selected for this study was conducted. A PCA model that compared the distribution of lipids and phytosterols in wild rice showed clear separation between samples that were distributed in the Minnesota area and those from California and Canada. This observation could be the result of differing climates and growing conditions, but further investigation would be required to fully determine the reasoning for these differences.
Previous chemometric analysis of plant material has demonstrated the impact of growing location on the lipidome. Tobacco leaves grown in three different geographic
35 locations within China were analyzed by LC-MS-based lipidomic analysis [104]. The growing conditions, specifically rainfall and temperature, were considered to be major factors that altered the level of unsaturation of the fatty acids present. Differences in climate have been investigated further through analyzing the lipidome of Arabidopsis thaliana (A. thaliana) [105]. Acclimation to cold climates is of importance to plants that grow in geographic areas subject to low and freezing temperatures. Lipidomic analysis revealed that A. thaliana adapted to colder temperatures through increasing total TAG content [105]. The majority of the TAGs formed in response to acclimation were unsaturated fatty acids [105]. It was hypothesized that increased fatty acid unsaturation within the TAGs is an adaptive strategy for plants [105]. Finally, the lipidome of
Saccharina latissima (S. latissima), an edible brown seaweed that contains polyunsaturated and ω-3 fatty acids, was analyzed after growth in France, Norway, and the United Kingdom [106]. A GC-MS-based lipidomic approach was employed to determine the geographical influences on this plant’s lipidome. PCA analysis showed clustering based on geographic location, which was supported by the data that each S. latissima plant had different lipidomic fingerprints which contributed to the separation observed in the model [106]. Overall, it is evident based on the literature available that lipidomic profiles of various plant species can be differentiated based on geographic differences using analytical platforms.
2.3.2 Phytosterols in wild rice
Since the masses of major phytosterols are in the range of 50-500 m/z (+) [25], a separate PCA model on the ions within this mass range was constructed to avoid the influences of TAG and diacylglycerols, the abundant neutral lipids, on the model.
36
Separation of wild rice from white and brown rice was still observed in the scores plot of the PCA model (Figure 2.3 A). Therefore, an S-plot (Figure 2.3 B) was created to study the chemometric features that were contributing to the separation observed in the PCA model. Interestingly, two prominent metabolites were presented during a database search:
3,5-stigastadiene and 3,5-campestadiene (Table 2.2). These compounds are derived from phytosterol precursors and were not present within the Oryza spp. samples. MS/MS fragmentation further confirmed the identities of these steradienes (Figure 2.4 A and B).
Resolving the presence of these compounds can be potentially contributed to the unique processing and phytosterol content of wild rice. Conversion of phytosterols into steradienes proceeds via a dehydration reaction that occurs during the processing of vegetable oils [107], where β-sitosterol is converted into 3,5-stigmastadiene and campesterol produces 3,5-campestadiene [108]. The presence of steradienes is a commonly used quality indicator in industry to assess the processing level of olive oil, where crude and cold-processed oils have the lowest concentration which increases linearly with processing temperature [107, 109]. In a similar fashion, heat is applied to rice in order to reduce its moisture content. The moisture content of brown rice [110] is half of the moisture content of wild rice [6] before the drying step. Wild rice also undergoes a unique fermentation step, where heat and moisture are applied to change the grain color from green to brown [6]. The additional processing that wild rice must undergo to yield a rice of similar final moisture content to that of brown rice represents a potential explanation for the formation of these steradienes in wild rice.
Interestingly, detection of the parent compounds of these steradienes proved challenging. Extracted ions of β-sitosterol and campesterol indicated that the signal
37 associated with these ions was very low. One possible explanation for this challenge could be the low ionization efficiency of phytosterol compounds. Therefore, investigation into other phytosterols reported to have high concentration in wild rice was performed.
This additional analysis provided confirmation that the signal of other phytosterols was possible using the LC-MS method used in this study.
Stigmasterol and γ-oryzanol are two phytosterols that have been previously reported in wild rice [25]. Stigmasterol is an unsaturated plant sterol that has anti- hypercholesterolemic activity through blocking the absorption of dietary cholesterol [111,
112]. In rats, stigmasterol feeding at 0.5% dietary stigmasterol supplementation for six weeks resulted in an 11% reduction in plasma cholesterol [113]. γ-Oryzanol is a mixture of steryl ferulates that act as a chain-breaking antioxidant that inhibits lipoperoxidation
[114]. In a randomized control trial of mildly hypercholsterolemic men, rice bran oil containing γ-oryzanol lowered plasma total cholesterol by 6.3% after dietary supplementation for four weeks [115]. Detection and quantification of these two phytosterols was possible using the LC-MS method employed in this study.
After confirmation of their presence in the wild rice samples using chemical standards, the concentration of γ-oryzanol and stigmasterol in the wild rice samples was quantified for comparison to white and brown rice. Overall, wild rice had a higher concentration of phytosterols than white and brown rice (Figure 2.5 A and B). The average concentration of γ-oryzanol in the rice samples were: 0.06 g γ-oryzanol/kg extracted lipid (white), 0.28 g/kg (brown), and 0.91 g/kg (wild). Data analysis using
ANOVA and Student’s t-test indicated significance of p<0.05 between Asian rice samples compared to wild rice samples. These values are somewhat higher compared to
38 what has been previously reported [25]. Stigmasterol concentration followed a similar pattern: 1.26 g stigmasterol/kg extracted lipid (white), 7.06 g/kg (brown), and 10.24 g/kg
(wild). After data analysis using ANOVA showed a significant p<0.05, between groups comparisons revealed that only white and wild rice had statistically different levels of this phytosterol.
2.4 CONCLUSION
LC-MS-based chemometric analysis was performed in both white and brown rice
(Oryza spp.) and wild rice (Zizania spp.). Both confirmation of previous research as well as novel insights were generated. Wild rice has a lipidome characterized by high content of essential fatty acids, ALA and LA. The positional information of the fatty acids within the TAGs identified here is a potential future direction for this research. Phytosterol content within wild rice and as part of a balanced diet can play a role in mitigating disease. Overall, wild rice represents an excellent opportunity for consumers to increase their consumption of many beneficial nutrients and non-nutrients.
39
Table 2.1 Composition of identified TAGs in rice. MS/MS fragmentation in conjunction with elemental analysis provided structural information for 19 TAGs detected in rice. ID Formula Calculated Measured Mass TAG Major Exact Mass Mass of Deviation Composition Fragments of + + of [M+NH4] [M+NH4] (ppm) MS/MS (m/z)
I C53H94O6 844.7394 844.7396 0.2 PPoLn 547, 571, 599
II C53H96O6 846.7551 846.7554 0.4 PPLn 551, 573
III C53H98O6 848.7707 848.7713 0.7 PPL 551, 575
IV C53H100O6 850.7864 850.7870 0.7 PPO 551, 577
V C55H94O6 868.7394 868.7396 0.2 PLnLn 573, 595
VI C55H96O6 870.7551 870.7556 0.6 PLLn 573, 575, 598
VII C55H98O6 872.7707 872.7729 2.5 PLL 575, 599
VIII C55H100O6 874.7864 874.7888 2.7 POL 575, 577, 601
IX C55H102O6 876.8020 876.8041 2.4 POO 577, 603
X C55H104O6 878.8177 878.8179 0.2 PSO 577, 579, 605
XI C57H92O6 890.7238 890.7249 1.2 LnLnLn 595
XII C57H94O6 892.7394 892.7399 0.6 LLnLn 595, 597
XIII C57H96O6 894.7551 894.7560 1.0 LLLn 597, 599
XIV C57H98O6 896.7707 896.7731 2.7 LLL 599
XV C57H100O6 898.7864 898.7858 -0.7 OLL 599, 601
XVI C57H102O6 900.8020 900.8040 2.2 OOL 601, 603
XVII C57H104O6 902.8177 902.8196 2.1 OOO 603
XVIII C57H106O6 904.8333 904.8330 -0.3 PLA 575, 607, 631
XIX C57H108O6 906.8490 906.8494 0.4 PSA 577, 607, 633 The sn-position of the three fatty acyl chains attached on the glycerol backbone of the TAG was not studied here. P: palmitic acid; Po: palmitoleic acid; S: stearic acid; O: oleic acid; L: linoleic acid; Ln: linolenic acid; A: arachidic acid.
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Table 2.2 Identities of detected steradienes in wild rice. After identification of two phytosterol dehydration products that contributed to the separation of Asian and wild rice, confirmation of their identities was performed using MS/MS fragmentation (Figure 2.4). ID m/z (+) Chemical Formula Name
XX 383.3679 C28H46 3,5-Campestadiene
XXI 397.3829 C29H48 3,5-Stigmastadiene
41
XIX XVII XVIII XVIII XV XVI XVI XVII XIV XIV XV XIII XIII
X X IX VIII IX VIII VII VI VII VI
III IV III IV II I II
100 874.7870 XVIII XVI XVII O XV 601.5201 XIV C18:1 XII XIII 577.5193 875.7894 XI C17H33 O 575.5039
% O O O O
602.5235 IX C16:0 601.5196 C18:2 VII VIII 577.5196 VI C15H31 C17H31 V 876.7924 603.5280 857.7593 0 m/z 575 600 625 650 675 700 725 750 775 800 825 850 875 III IV II
Figure 2.1 Representative two-dimensional chromatograms of white (A), brown (B), and wild rice (C). Representative fragmentation pattern of triacylglycerols from MS/MS fragmentation (D). Each TAG species in panels A-C has multiple bands which + + correspond to the [M+NH4] and [M+Na] adducts generated during electrospray ionization.
42
p[Comp. 1]/p(corr)[Comp. 1]
1.0 XV 80 White V Brown IX VI XIII 0.8 XII 60 Wild II 0.6 XI 40 0.4 XIV 20 0.2 0 t[2] -0.0 p(corr)[1] -20 -0.2 VII
-40 I -0.4 XIX X -60 -0.6
-0.8 VIII -80 XVI III XVII IV -1.0 XVIII -200 -150 -100 -50 0 50 100 150 200 t[1] -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 p[1]
Figure 2.2 PCA and S-plot modeling [mass ≥ 500 m/z (+)] reveals major contribution of TAGs in separating wild rice and Asian rice. To capture the influence of TAGs, a mass range ≥ 500 m/z (+) is featured in this figure. A PCA model illustrates separation of all three rice samples. B S-plot labeled with TAG species identified through MS/MS fragmentation (Table 2.1). C HCA-based heat map of TAG relative abundance throughout rice varieties. P: palmitic acid; Po: palmitoleic acid; S: stearic acid; O: oleic acid; L: linoleic acid; Ln: linolenic acid; A: arachidic acid.
43
120 1.0 White 100 XX XXI Brown 0.8 80 Wild 0.6 60
40 0.4
20 0.2 0 t[2] -0.0 -20 p(corr)[1]
-40 -0.2
-60 -0.4 -80 -0.6 -100
-120 -0.8 -140 -120 -100 -80 -60 -40 -20 0 20 40 60 80 100 120 140 t[1] -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 p[1]
Figure 2.3 PCA and S-plot modeling [mass range < 500 m/z (+)] reveals major contribution of phytosterol dehydration products in separating wild and Asian rice. To exclude the influence of TAGs, a mass range < 500 m/z (+) is featured in this figure. A PCA model illustrates separation of rice varieties. B S-plot labeled with steradienes compounds identified through MS/MS fragmentation (Table 2.2).
44
100 397.3806
175 %
147.1161 135.1162 161.1315 133.1005 396.3677 175.1467 0 m/z 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400
100 383.3648
175 %
384.3682 147.1160
135.1159 161.1313
133.1002 327.0767 175.1466 383.1378 385.3714 189.1624 215.1774 243.2086
0 m/z 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400
Figure 2.4 MS/MS fragmentation and proposed fragmentation pathway for two steradienes identified in wild rice. Due their similar structures, both compounds had similar fragmentation patterns with ions at m/z (+) 175, 161, and 147. A Fragmentogram of 3,5-stimastadiene, a dehydration product of β-sitosterol. B Fragmentogram of 3,5- campestadiene, a dehydration product of campesterol.
45
Figure 2.5 Quantification of two detected phytosterols within extracted lipids of rice. A Quantification of γ-oryzanol. B Quantification of stigmasterol.
46
CHAPTER 3: CHEMOMETRIC ANALSYIS OF INTENSE PULSED LIGHT-
ELICITED CHEMICAL CHANGES IN MESQUITE FLOUR
47
3.1 INTRODUCTION
Radiation is a physical phenomenon of transferring energy through space or matter [116]. The process through which radiation is applied is irradiation. In food manufacturing, both ionizing and non-ionizing radiations are used to disinfect foods for consumer safety and to decrease the use of chemical agents. The major benefits of radiation includes the extension of shelf life and a reduction in the loss of food quality
[116]. While the American Dietetic Association’s position is that food irradiation is safe, consumers still have reservations as a result of the implications of using radiation in food processing [117]. Sources of ionizing radiation in food disinfection are gamma rays, electron beams, and X rays [53, 116]. The dose of radiation delivered to the food matrix, measured in gray (Gy, J/kg), can be between 50 Gy to 100 Gy [118]. Intense pulsed light
(IPL) is a non-thermal and non-ionizing disinfection method that can be used in food post-production and pre-distribution processing to reduce the incidence of contamination with foodborne microbes. The need to develop IPL is based on the fact that the ionizing radiation, which is approved for use in food applications, has negative impacts on food quality and low consumer acceptance.
After exposure to ionizing radiation, nutrients undergo chemical changes [119].
Carbohydrates are converted from their complex forms into more simple forms; however, this effect is minor and does not translate into nutritional significance [120]. Lipid oxidation after processing is dependent upon unsaturation level of fatty acids, temperature during processing, and baseline level of oxidation prior to processing [121].
Oxidation, polymerization, decarboxylation, and dehydration occur as a result of ionizing radiation treatment on lipids [120]. In order to address these effects, antioxidants such as
48
α-tocopherol can be introduced to the food matrix prior to processing [122]. Protein and amino acids are not as susceptible to changes of nutritional consequence when treated with radiation [120].
Powdered foods have a high surface area to volume ratio, and are ideal for IPL treatment due to the challenge that depths greater than 1 mm present to this technology
[62]. Mesquite pod flour is a common powdered ingredient used in traditional Mexican baking [123]. As the fruit of the mesquite tree (Prosopis spp.), the pods are milled to produce a mesquite flour [124]. Mesquite flour can be used to replace wheat flour in common bakery items, as well as fermented to produce alcoholic beverages [125].
Mesquite flour is high in protein content at about 10% [124, 125], but is deficient in sulfur-containing amino acids, including methionine and cysteine [126]. Fiber content of mesquite flour is between 27-32% of pod weight [127], and its fat content is 3.5% [125].
The fatty acid composition of mesquite flour has been previously studied [128].
Polyunsaturated fatty acids account for 55.5% of total fatty acids, in comparison to 25.7% saturated fatty acids and 18.9% monounsaturated fatty acids. The top three fatty acids within mesquite flour are linoleic acid (C18:2), oleic acid (C18:1), and palmitic acid
(C16:0) [128, 129]. At the drying temperature of 50 °C, unsaturated fatty acids in mesquite flour are subject to potential oxidation [128], such as autoxidation from the exposure to light and air, leading to the decomposition of these fatty acids and the triacyclglycerols in which they are incorporated [130]. Mesquite flour also contains antioxidants, including tocopherols and phenolic compounds [131, 132]. There has been previous study on the use of food irradiation on mesquite flour. The presence of animals and insects in the fields where mesquite pods are grown introduces the possibility of
49 contamination with bacteria that is pathogenic to humans [133]. After treatment with 10 kGy of ionizing radiation, mesquite flour total plate count was reduced from 4.5-5.7 log
CFU/g to at or below the level of detection of 1 log CFU/g [133]. Hexanal, an indicator of lipid oxidation, was increased after treatment with ionizing radiation, indicating that this type of treatment degraded the lipid content of the mesquite flour pod [133].
IPL technology has been used to disinfect mesquite pod flower. Due to the high lipid and protein content of mesquite flour and the energy associated with IPL technology, it is possible that lipid oxidation and amino acid degradation could occur as the consequences of IPL treatment. Therefore, a comparison of the chemometric profiles of IPL- and γ-irradiation-treated mesquite flour will be useful for understanding the chemical changes in mesquite flour. In this study, untargeted and targeted chemometric analyses of irradiated mesquite flour were conducted to examine the chemical changes associated with lipid oxidation and amino acid degradation.
3.2 MATERIALS AND METHODS
3.1.1 Samples and chemicals
Mesquite flour was provided by collaborators at the U.S. Department of
Agriculture (Eastern Regional Research Center, Wyndmoor, PA). IPL treatments were prepared using a X-1100 Steripulse-XL system (Xenon Corporation, Woburn, MA) consisting of a 76 cm. linear xenon flash lamp, a vibratory feeder (Eriez Manufacturing
Co., Erie, PA), two inline duct mixed flow fans connected to a thermostatic circulating water bath (LabX, Midland, ON, Canada), a volumetric feeder (Tecnetics Industries, Inc.,
St. Paul, MN), an ultrasonic humidifier/dehumidifier, a nitrogen gas cylinder, and an infrared heater. The IPL lamp produced wavelengths between 190-1100 nm. Samples
50 were treated with a pulse rate of 1 Hz, pulse duration of 243 µs, voltage of 3000 V, and energy of 700 J/pulse. The residence time under the IPL lamp was 28 s. Humidity was set to 35-40 %. The processing temperature was maintained at 56±1°C. Mesquite flour was prepared by collaborators with γ-irradiation using a 137Cesium radiation source at a dose rate of approximately 0.07 kGy per minute. The total radiation dose samples received was 2, 4, 6, and 8 kGy.
The control and treated mesquite flour powder were extracted by LC-MS-grade methanol from Avantor Performance Materials (Radnor, PA) at a ratio of 1:10 (w:v).
After vortexing and 5-min sonication, the samples were centrifuged at 21,000 × g for 10 min. The supernatant was transferred to a new 1.5 mL Eppendorf tube and stored at -20
°C for further analysis. Triphenylphosphine (TPP), and 2-hydrazinoquinoline (HQ) were purchased from Alfa Aesar (Ward Hill, MA); 2,2’-dipyridyl disulfide (DPDS) from MP
Biomedicals (Santa Ana, CA); LC-MS-grade water and acetonitrile from Fisher
Scientific (Houston, TX).
3.1.2 Sample preparation
Chemical derivitization, LC-MS analysis, and multivariate modeling and data visualization were adapted as previously described [134]. Prior to the liquid chromatography-mass spectrometry (LC-MS) analysis, the methanol extract was first derivatized by HQ using a modified method [135]. Briefly, 2 µL of previously extracted mesquite flour powder was added into 100 µL of acetonitrile solution containing 1 mM
DPDS, 1mM TPP, 1 mM HQ, and 10 µM d4-acetic acid (internal standard). After the incubation at 60 °C for 30 min, the reaction was terminated by chilling sample on ice and
51 the addition of 100 μL of water. After vortexing, the mixture was centrifuged at 21,000 × g for 10 min, and the supernatant was transferred into a HPLC vial for LC-MS analysis.
3.1.3 LC-MS Analysis
A 2 µL sample aliquot was injected into an Acquity ultra-performance liquid chromatography (UPLC) system (Waters, Milford, MA) and separated in a BEH C18 column. Mobile phases used were A: 0.1% formic acid in water, and B: 0.1% formic acid in 100% methanol. The LC eluate was introduced into a Synapt-G2-Si quadrupole time-of-flight mass spectrometer (QTOFMS, Waters) for accurate mass measurement and ion counting. Capillary voltage and cone voltage for the electrospray ionization were maintained at 3kV and 30V, respectively, for positive-mode detection. Source temperature and desolvation temperature were set at 120 and 350 oC, respectively.
Nitrogen was used as both the cone gas (50 L/h) and desolvation gas (600 L/h). For accurate mass measurement, the mass spectrometer was calibrated with sodium formate solution with a mass-to-charge ratio (m/z) of 50-1200 and monitored by the intermittent injection of the lockmass leucine enkephalin ([M+H]+ = m/z 556.2771) in real time. Mass chromatograms and mass spectral data were acquired and processed by MassLynx software (Waters) in centroided format.
3.1.4 Multivariate modeling and data visualization
After data acquisition in the UPLC−QTOFMS system, the chromatographic and spectral data of samples were deconvoluted by MarkerLynx software (Waters). A multivariate data matrix containing information on sample identity, ion identity (retention time and m/z), and ion abundance was generated through centroiding, deisotoping, filtering, peak recognition, and integration. The intensity of each ion was calculated by
52 normalizing the single-ion counts (SIC) versus the total-ion counts (TIC) in the whole chromatogram. The data matrix was exported into SIMCA-P+ software (Umetrics,
Kinnelon, NJ) and transformed by Pareto scaling. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) was then performed to model the data matrix and define the correlations among the samples. The compounds contributing to the sample separation in the model were identified in the loadings plot of the model.
3.3 RESULTS AND DISCUSSION
3.2.1 Targeted analysis
Comparison between a representative IPL sample and the γ-irradiated samples showed light-induced changes in selected short chain fatty acids. While there were no significant changes to acetic acid concentration after both treatments, a clear trend emerged in the γ-irradiated samples (Figure 3.1 A). The fate of propionic acid (Figure
3.1 B) was different, as all samples were affected by treatment in a significant manner.
Overall, the trend observed in acetic acid and the degradation of propionic acid coupled to provide insight as to the alteration in these fatty acids during treatment with non- thermal processing.
3.2.2 Untargeted chemometric profiling
Targeted analysis was unable to fully capture the suspected differences in treatments. Therefore, a more comprehensive analysis was performed. Whether IPL and
-radiation treatments could cause chemical changes in mesquite flour was evaluated using untargeted chemometric analysis. A PCA model based on the LC-MS analysis of methanol extracts of the mesquite flour samples after IPL and γ-irradiation treatment
53 showed the separation of γ-irradiated and IPL-treated samples from the untreated control in a dose-dependent pattern (Figure 3.2 A). The markers increased by IPL and - radiation treatments (I-IX) were identified in the S-plot of an OPLS-DA model, and further characterized by database search and structure confirmation using chemical standards if available (Figure 3.2 B and Table 3.1).
Oxidized fatty acids, including 9-hydroxy-octadecadienoic acid (9-HODE), octadecanedioic acid, and trihydroxy-octadecenoic acid, were identified among these markers (Table 3.1). Fatty acids, esterified to a glycerol backbone, form triacylglycerols
(TAG) that, in foods, are susceptible to attack by radical oxygen, forming products that affect the rancidity of the food matrix [136]. Polyunsaturated fatty acids (PUFA) are especially subject to attack by radical oxygen; therefore, foods with high levels of PUFA are at greater risk of lipid oxidation than their saturated counterparts [136]. When a food matrix is processed with γ-radiation, the lipid content is degraded. Less is understood about the effect that IPL can have on the lipid content within food.
Quantitative analysis of 9-HODE showed that IPL treatment significantly increased the concentration of 9-HODE in mesquite flour (Figure 3.2 C). This observation indicated that IPL treatment can cause lipid oxidation. However, this increase of 9-HODE (~5 µg/g) after IPL treatment may not be biologically significant. Linoleic acid, the parent compound of 9-HODE, is the most abundant fatty acid present in mesquite flour which contains 1.87-2.64 g total fat/100 g powder [128]. Therefore, it is unlikely that other PUFA are affected in a manner more detrimental than that of the conversion of linoleic acid to 9-HODE. Further, α-ketoisovaleric acid was significantly increased by IPL and 2-, 6-, and 8-kGy treatments (Figure 3.2 D). Interestingly, α-
54 ketoisovaleric acid has been previously identified as a deamination product of valine in γ- irradiation-treated protein samples [137]. Therefore, these two markers indicate that these two non-thermal disinfection methods could selectively affect fatty acids and amino acids in mesquite flour.
3.4 CONCLUSION
As the novel IPL technology is further investigated, its effect on food will become better understood. In this study, IPL technology was compared to γ-irradiation. The effects of these two processing methods were evident in mesquite flour, as lipid oxidation and amino acid degradation were observed with LC-MS-based chemometric analysis.
Further investigation into other food matrices is needed to understand the full capability of IPL treatment.
55
Table 3.1. Chemical markers separating IPL and γ-irradiation treatments from control. ID Chemical Identity m/z (-) Chemical Formula
* I α-ketoisovaleric acid 115.0391 C5H8O3 * II Phenyllactic acid 165.0546 C9H10O3 * III 2-Isopropylmalic acid 175.0601 C7H12O5 # IV Caffeic Acid 179.0335 C9H8O4 * V Nonate 187.0960 C9H16O4 # VI HODE 295.2264 C18H32O3 * VII Octadecanedioic acid 313.2372 C18H34O4 * VIII Trihydroxy-octadecenoic acid 329.2319 C18H34O5 * IX Phosphoethanolamine (18:2) 476.2774 C23H44NO7P #: markers confirmed by chemical standards. *: markers identified by database search.
56
Figure 3.1 Concentration of short-chain fatty acids in mesquite flour after IPL- and -irradiation treatments. A. Acetic acid concentrations. B. Propionic acid concentrations.
57
1.0 I IV V 40 0.8 VII VII I 30 0.6 II III 20 -irradiation 0.4 IX VI 0.2 10 -0.0 0 t[2] IPL -0.2 -10 -0.4 -20 Control -0.6 -30 -0.8 -40 -1.0 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 -0.15 -0.10 -0.05 -0.00 0.05 0.10 0.15 t[1]
Figure 3.2 Chemometric analysis of potential chemical changes in mesquite flour after IPL- and -irradiation treatments. A. Score plot of a PCA model on all treatment groups. B. S-plot of an OPLS-DA model on the comparison between untreated control and treatments. The prominent markers increased by either IPL or -irradiation annotated were identified and labeled (Table 3.1). C: Concentration of 9-HODE. D. Relative abundance of α-ketoisovaleric acid.
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