Changes in Protein-Water Dynamics Impact the Quality of Chicken Meat Post Freezing

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

John Charles Frelka, M.S.

Graduate Program in Food Science and Technology

The Ohio State University

2017

Dissertation Committee:

Dr. Dennis R. Heldman, Advisor Dr. Farnaz Maleky Dr. Sudhir K. Sastry Dr. Yael Vodovotz Dr. Macdonald P. Wick

Copyrighted by

John Charles Frelka

2017

Abstract

Freezing is one of the most common food preservation methods in the modern world.

This method relies on the phase change of liquid water to solid ice, thus reducing the mobility of the system and, in combination with lowered temperature, reducing chemical and biological reactions detrimental to food quality. However, the formation of ice crystals can disrupt the structure of the food matrix, causing loss of quality. Protein-based foods, such as meat, are unique physical systems that have different challenges than fruit and vegetable products. The overall objective of this research was to better understand and measure the physical changes in protein-water interactions in chicken during the freezing process and during frozen storage. The specific objectives aimed to understand the effects of freeze time, storage temperature, and freeze/thaw abuse.

The effect of characteristic freeze time (CFT) on the physical quality of chicken meat proteins was explored using typical quality measurements and thermal analyses. CFT ranged from 2.4 to 104 min compared to an unfrozen control. Total moisture, protein extraction, brine uptake, myofibrillar fragmentation index, enthalpy, and gelation were measured after samples were thawed. The total enthalpy as well as relative contributions of each peak were significantly higher after freezing, regardless of CFT. The gelation of salt soluble proteins varied with CFT and the gels had significantly different G’ between

ii the fast and medium freezing CFT. There was a 40% decrease in the final G’ between the unfrozen and slow frozen samples.

The effect of frozen storage temperature was explored in an aqueous extract model system and in whole meat systems. The model system was used to study myoglobin oxidation. This study found that under frozen conditions there was a rate acceleration with a maximum around −20°C. This effect was magnified by the addition of NaCl to the system. Whole meat products were tested to determine if this effect was evident in these systems. Quality was measured in whole chicken breasts and ground chicken patties over

3 months of storage. Temperature did not significantly impact any attribute measured.

Two freezing rates were tested in patties but did not result in different rates of quality loss. The reverse stability evident in the model system was not observed in the chicken products.

The impact of freeze/thaw abuse on the quality of chicken was assessed using magnetic resonance imaging. Drip loss was the only measured attribute that showed significant change with increased freeze/thaw cycles. Physical distribution of water within the chicken breasts was observed in unbrined samples as freeze/thaw cycles increased. There were significant shifts in the proton density distributions from MRI images. Differences were not as pronounced in brined samples, suggesting a level of cryoprotection conferred by the brine. In both brined and unbrined samples, only small differences in T2 distributions were observed. Using NMR micro-imaging significant shifts in T2 distributions were observed in unbrined samples. Water plays a critical role in

iii the structure and quality of chicken meat. This research adds to the understanding of how freezing impacts these critical protein-water interactions.

Acknowledgments

I greatly appreciate the guidance of Dr. Dennis R. Heldman through the process of the completion of this degree. His experience has been an invaluable resource and I am grateful for the opportunity to learn from him.

Thank you to my committee members, Drs. Maleky, Sastry, Vodovotz, and Wick.

Special thanks to Dr. Wick for teaching me how to think like a meat scientist and providing guidance through this field I knew nothing about. Special thanks also to Dr.

Vodovotz for teaching me the ways of physical properties and how to find the proper instrument to solve any problem.

Thanks to the Ohio Agricultural Research and Development Center SEEDS program for funding part of this research. Thanks also to the Dale A. Sieberling research endowment for providing the laboratory funds that made much of this research possible.

This project would not have been possible without the efforts of my labmates, particularly Mr. David Phinney, who provided as much guidance as friendship, and Ms.

Sravanti Paluri, my lab bro from beginning to end and helping write my codes. Thanks to all though who assisted in the data acquisition, in particular Jace Metzcar. Thanks also to those at The Ohio State University’s Meat Laboratory, particularly Mr. Tom Katen and

Mr. Ron Cramer for their help and accommodation. Thanks to Dr. Huyen Nguyen for her help running the MRI scans and Dr. Xiangyu Yang for his help in the analysis of the MRI

v data. Thanks to the staff at the Campus Chemical Instrumentation Center NMR facilities, particularly Drs. Chunhua Yuan and Tanya Whitmer. Those companies who provided materials are greatly appreciated including Gerber’s Poultry and West Liberty Foods.

Vita

2007...... San Joaquin Memorial High School

2011...... B.S. Food Science and Technology,

University of California, Davis

2013...... M.S. Food Science and Technology,

University of California, Davis

2013 to present ...... Graduate Research Associate, Department

of Food Science and Technology, The Ohio

State University

Publications

Phinney, D.M., J.C. Frelka, D.R. Heldman. 2016. Chemical-free neutralization of caustic peeled tomato slurry to reclaim wastes. Food Bioprod. Process. 100:545-550

Phinney, D.M., J.C. Frelka, J.L. Cooperstone, S.J. Schwartz, D.R. Heldman. 2017. Effect of solvent addition sequence on lycopene extraction efficiency from membrane neutralized caustic peeled tomato waste. Food Chem. 215:354-361

Phinney, D.M., J.C. Frelka, D.R. Heldman. 2017. Composition based prediction of temperature dependent thermophysical food properties: Reevaluating component groups and prediction models. J. Food Sci. 82:6-15.

Fields of Study

Major Field: Food Science and Technology

vii

Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vii

Publications ...... vii

Fields of Study ...... vii

Table of Contents ...... viii

List of Tables ...... xiv

List of Figures ...... xv

CHAPTER 1: Introduction ...... 1

1.1 Objectives ...... 3

CHAPTER 2: Literature Review ...... 4

2.1 Meat – Definition and Structure ...... 4

2.2 Meat Quality ...... 7

2.2.1 Fresh Meat Products ...... 7

2.2.2 Processed Meat Products ...... 13

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2.3 Measurement of Meat Quality ...... 15

2.3.1 Typical Methods ...... 16

2.3.2 Advanced Methods ...... 21

2.4 Influence of Freezing on Meat Quality ...... 31

2.4.1 Mechanisms of Water Holding in Meat ...... 31

2.4.2 Freezing Rate ...... 33

2.4.3 Frozen Storage Temperature ...... 36

2.5 Conclusion ...... 40

2.6 REFERENCES ...... 42

CHAPTER 3: Impact of Characteristic Freeze Time on Functionality of Chicken Breast

(Pectoralis major) Meat ...... 53

ABSTRACT ...... 53

3.1 INTRODUCTION ...... 54

3.2 MATERIALS and METHODS...... 55

3.2.1 Freezing methods...... 55

3.2.2 Brine uptake...... 56

3.2.3 Rheology...... 56

3.2.4 Nuclear Magnetic Resonance Cross Relaxation...... 57

3.2.5 Gel electrophoresis...... 57

3.2.6 Moisture Content...... 58

3.2.7 Myofibrillar extract preparation...... 58

3.2.8 Differential Scanning Calorimetry...... 59

3.2.9 Myofibrillar fragmentation index...... 59

3.2.10 Statistical analysis...... 60

3.3 RESULTS and DISCUSSION ...... 60

3.3.1 Water binding...... 60

3.3.2 Gel-formation...... 61

3.3.3 DSC enthalpies...... 63

3.4 CONCLUSION ...... 65

3.5 TABLES and FIGURES ...... 67

3.6 REFERENCES ...... 73

CHAPTER 4: Kinetics of physical quality loss in a model meat system and whole meat products under frozen storage conditions ...... 77

Abstract ...... 77

4.1 Introduction ...... 78

4.2 Materials and Methods ...... 81

4.2.1 Preparation of dehydrated sarcoplasmic extract...... 81

4.2.2 Kinetic assay...... 81

4.2.3 Chicken samples...... 83

4.2.4 Freezing and frozen storage...... 83

4.2.5 Water holding capacity...... 84

4.2.6 Drip loss...... 85

4.2.7 Cook loss...... 85

4.2.8 Statistical Analysis...... 85

4.3 Results and Discussion ...... 86

4.3.1 Kinetics of myoglobin oxidation in a model system ...... 86

4.3.2 Effect of storage temperature on quality of frozen chicken breast meat. ... 89

4.4 Conclusion ...... 95

4.5 Tables and Figures ...... 97

4.6 REFERENCES ...... 102

CHAPTER 5: Assessment of chicken meat quality after freeze/thaw abuse using magnetic resonance imaging techniques ...... 105

5.1 Introduction ...... 105

5.2 Materials and Methods ...... 107

5.2.1 Chicken meat samples...... 107

5.2.2 Freeze/thaw protocol...... 108

5.2.3 Drip loss...... 108

5.2.4 Water holding capacity...... 109

5.2.5 Brine uptake...... 109

5.2.6 Cook loss...... 110

5.2.7 Texture...... 110

5.2.8 NMR Micro-imaging...... 111

5.2.9 Magnetic resonance imaging...... 111

5.2.10 Statistical analysis...... 112

5.3 Results and Discussion ...... 113

5.3.1 Quality measurements...... 113

5.3.2 MRI imaging...... 114

5.3.3 NMR Micro-imaging...... 117

5.3.4 Correlation of quality attributes and magnetic resonance measurements . 119

5.4 Conclusion ...... 119

5.5 TABLES and FIGURES ...... 121

5.6 REFERENCES ...... 135

CHAPTER 6: Conclusion ...... 137

CHAPTER 7: Future Work ...... 139

7.1 Freezing rate...... 139

7.2 Frozen storage temperature ...... 139

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7.3 Chicken quality and magnetic resonance techniques...... 140

Appendix A: SAS code for Monte Carlo simulation of kinetic data ...... 141

Appendix B: SAS code for fitting of normal distributions to T2 distributions from MRI image analysis ...... 147

REFERENCES ...... 150

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List of Tables

Table 3.1. Summary of measured quality attributes for samples of chicken breast meat frozen at different rates. Within columns, values with the same letter superscript are not significantly different (α = 0.05). Lack of superscripts indicate no significant difference across any treatment...... 67

Table 4.1. Statistical evaluation of reaction order fit to quality attributes in frozen chicken meat products. These reaction orders are used to calculate the rate constants in

Tables 3 and 4...... 97

Table 4.2. Rate constants calculated for the change in quality attributes in whole chicken breasts...... 98

Table 4.3. Rate constants calculated for the change in quality attributes in ground chicken patties...... 98

Table 5.1. Average T2 values extracted from MRI and NMR micro-imaging T2 maps. 121

Table 5.2. Correlation matrix for measured quality attributes and magnetic resonance parameters...... 122

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List of Figures

Figure 2.1. Schematic overview of muscle structure (Modified from OpenStax, 2014). .. 5

Figure 2.2. Structural components of the sarcomere (Tornberg, 2005)...... 6

Figure 2.3. Flowchart of reactions leading to color changes in fresh meat...... 8

Figure 2.4. Probes used in typical meat texture analysis methods: (A) Warner-Bratzler,

(B) Allo-Kramer, and (C) Blunt Meullenet-Owens Razor shear. (Photos from Texture

Technologies Corp.) ...... 19

Figure 2.5. Idealized thermogram for meat showing the three primary transitions: (I) myosin, (II) sarcoplasmic proteins and collagen, and (III) actin. (Tamilmani & Pandey,

2016) ...... 21

Figure 2.6 Example DTG curves of soy and wheat bread doughs (a) and the raw ingredients used to make these doughs (b). (Simmons et al., 2012) ...... 23

Figure 2.7. T2 map of rabbit muscle (a) 45 min, (b) 105 min, (c) 225 min, and (d) 345 min post-mortem. Arrows indicate localization of high-mobility water withing the muscle sytem. (Bertram, et al., 2004) ...... 27

Figure 2.8. SEM (A & B) and TEM (C & D) images from fast (A & C) and slow (B &

D) frozen pork (Yu et al., 2010) ...... 30

Figure 2.9. Schematic illustrating changes in the structure of myosin as a function of freezing. After freezing and thawing the capillary action between the myosin chains is reduced causing the expulsion of water, which is not resorbed into the matrix...... 32

Figure 2.10. Illustration of freeze-concentration. The dark blue represents the unfrozen water fraction, light blue represents ice, and red circles represent dissolved solutes...... 37

Figure 2.11. Effect of initial diameter (Do) and temperature on time to reach D1 (60 µm) in beef semitendinosus (From Zaritzky & Martino, 1988) ...... 39

Figure 3.1. Representative curves of G' over a temperature sweep from 40 to 80°C of chicken SSPs extracted from chicken breast samples subjected to different freezing rates.

...... 68

Figure 3.2. Proposed mechanism for modification of myosin as a function of freezing and thawing...... 69

Figure 3.3. Proposed mechanism of reduced gel strength between unfrozen (A) and previously-frozen (B) chicken meat. Modifications to the myosin head groups after freezing result in a higher initial G’, but the initial gel is weaker, causing the decrease around 50°C. The final gel network is not as tight, resulting in a lower final G’...... 70

Figure 3.4. Cross-relaxation spectra for thermally-induced meat gels formed from the

SSP fraction of chicken breast meat frozen at different rates...... 71

xvi

After freezing the increase in the second peak shows a co-precipitation of sarcoplasmic proteins onto the myofibrillar proteins...... 72

Figure 4.1.Arrhenius plots of rate constants calculated for the oxidation of oxymyoglobin in sarcoplasmic extract with 0% (A) and 0.5% (B) added NaCl. Black lines and markers indicate experiments performed under unfrozen conditions while gray lines and markers indicate the system was frozen...... 99

Figure 4.2. Plot of activation energy of oxymyoglobin oxidation as a function of added

NaCl at temperatures above the initial freezing temperature...... 100

Figure 4.3. Comparison of model using only elevated salt data from above freezing

(solid line) to model created from full dataset with 0% added salt (dashed line)...... 101

Figure 5.1. Changes in drip loss in unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05). 123

Figure 5.2. Water holding capacity measured by press method for unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05)...... 124

Figure 5.3. Brine uptake by unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05). 125

Figure 5.4. Cook loss from unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05). 126

Figure 5.5. BMORS texture shear force (A) and shear energy (B) of unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05)...... 127

xvii

Figure 5.6. T1-weighted images from unbrined and brined chicken breasts after 0, 1, and

2 subsequent freeze/thaw cycles...... 128

Figure 5.7. Proton density images from unbrined and brined chicken breasts after 0, 1, and 2 subsequent freeze/thaw cycles...... 129

Figure 5.8. Distributions of pixel intensity from proton density images of unbrined (A) and brined (B) chicken breasts subjected to 0, 1, or 2 freeze/thaw cycles...... 130

Figure 5.9. T2 mapping images from unbrined and brined chicken breasts after 0, 1, and

2 subsequent freeze/thaw cycles...... 131

Figure 5.10. T2 distributions from T2 maps of unbrined (A) and brined (B) chicken breasts subjected to 0, 1, or 2 freeze/thaw cycles...... 132

Figure 5.11. Proton density images for unbrined chicken breast samples subjected to 0

(A), 1 (B) or 2 (C) freeze/thaw cycles. Higher signal intensity is indicative of higher mobility water. Gaps appearing in images are indicative of micro-channels forming between muscle cells...... 133

Figure 5.12. Average T2 distributions obtained from T2 maps of unbrined chicken meat samples using NMR micro-imaging...... 134

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CHAPTER 1: Introduction

Humans have been taking advantage of the benefits of freezing for thousands of years, recognizing that food, when frozen solid during the winter season, remained edible for long periods of time (Jul, 1984; National Frozen & Refrigerated Foods Association,

2009). Freezing is one of the major forms of food preservation in the modern world allowing the extension of the shelf-life of many foods for up to a year or more. Freezing works in two primary ways to preserve food: first, it employs low temperatures and second, it causes ice crystals to form, resulting in less free water. Both of these processes are synergistic in altering the rate of chemical reactions and retarding the growth of spoilage and pathogenic microorganisms. Freezing is not without its limitations as it can cause structural damage, protein denaturation, and loss of functionality (Pérez-Chabela and Mateo-Oyagüe, 2004). In meat systems this can result in the decreasing downstream processing capabilities, including water binding functions, which are critical to the production of high-quality processed muscle-based foods.

The frozen food industry is a global industry with annual sales of $192 billion in 2010 and is projected to exceed $306 billion by 2020 (Allied Market Research, 2015). Though frozen fruits and vegetables are one of the most recognizable segments of the frozen food market, many types of meat are stored and distributed as frozen products. Many meat products are also stored frozen before being further processed into other products. Meat

1 products are a major segment of the frozen food industry, accounting for 41% of the market (ReportLink, 2013). Although meat may or may not be purchased frozen, many meat products are frozen at some point during storage and handling.

The recommended temperature for frozen storage established in the 1950s is −18°C

(0°F) and is still the current recommended storage temperature for frozen food (Frozen

Food Handling and Merchandising Alliance, 2009). This temperature was found to keep a majority of food products at an acceptable level of quality for up to one year. However, this standard has been called into question in the literature (S. S. James & James, 2012).

The data used to generate this recommendation, it has been pointed out, is based on outdated technology and data (Pérez-Chabela and Mateo-Oyagüe, 2004). Similarly, Jul

(1982) pointed out that many of the current practices in the frozen food industry are based on limited studies and should not necessarily be applied broadly to all foods indiscriminately.

A large amount of energy is required to freeze as well as to keep the frozen product at such low temperatures during the entire storage period (Zanoni & Zavanella, 2012).

Recent trends in consumption indicate that frozen foods are not being stored for as long as they once were. Based on this observation, it may be possible to raise commercial frozen storage temperatures without sacrificing high-quality food at the time of consumption. Raising freezer temperatures would mean less energy is required during storage, which would result in reduced energy costs and lower environmental impact.

However, before these changes can be implemented, it is critical to understand the impact these changes would have on the final product quality.

1.1 Objectives

The overall objective of this dissertation is to better understand and measure the physical changes in protein-water interactions in a muscle food during the freezing process and during frozen product storage. The specific objectives of are the following:

1. To determine the impact of freezing rate on the physical quality of chicken

breast meat.

2. To determine the effect of elevated frozen storage time and temperature on

meat quality.

3. To determine the effect of freeze/thaw cycling on the water mobility and

localization in chicken breast meat and the effects on quality.

CHAPTER 2: Literature Review

2.1 Meat – Definition and Structure

Meat is most broadly defined as “the edible flesh of animals used as food” (Lawrie &

Ledward, 2006b). This literature review will primarily focus on meat from mammals

(cow, pig, sheep) and poultry (chicken, turkey). In order to understand the processes involved in the generation of meat and the subsequent quality of meat products, we must understand the structure of the meat as muscle. Figure 2.1 illustrates a generalized structure for skeletal muscles, the predominant source of meat. The muscle is composed of muscle bundles, which in turn are composed of muscle cell, myofibers, which are composed of myofibrils, themselves composed of thousands of tandemly assembled sarcomeres, the functional unit of muscle. In between the muscle fibers, a network of connective tissue, composed primarily of collagen, is responsible for holding the fibers together as well as attaching the muscle to the skeleton (Listrat et al., 2016). The sarcomere is composed of thick (myosin-based) and thin (f-actin-based) filaments (Figure

2.2). The myosin molecules are held together by the tail end at the M line. The myosin head groups are free to bind to the actin thin filament during contraction. This highly- organized fiber structure is responsible for the unique physical properties associated with meat quality, including texture and water holding capacity.

Figure 2.1. Schematic overview of muscle structure (Modified from OpenStax, 2014).

Figure 2.2. Structural components of the sarcomere (Tornberg, 2005).

The processes involved in the conversion of animal muscle into meat are complex and involve an array of physical and biochemical processes, primarily glycolysis and proteolysis (Shen & Du, 2016). The major changes involved include a decline in pH, dissipation of heat, rigor mortis, and proteolysis. Glycolysis is responsible for the decline in pH as the lack of oxygen triggers anaerobic glycolysis and the production of lactic acid and [H+] from the hydrolysis of ATP, during the onset of rigor mortis (Scheffler et al.,

2013). A slow dissipation of heat from the carcass may result in excessive protein denaturation (in conjunction with the lowered pH), which can result in defects in the final product. Rigor mortis is a toughening of the meat as a result of the depletion of residual adenosine triphosphate (ATP) causing permanent cross-bridges between actin and myosin. Proteolysis during post-mortem aging of meat results in the tenderness expected in meat as protease systems break down the structure of the myofibrils, primarily by breakdown of the Z disk structure. Genetics, variations in the pre- and post-slaughter conditions, as well as further processing steps, can have major impact on the overall quality of meat, as perceived by the consumer. The following sections will address the factors involved in determining the quality of meat.

2.2 Meat Quality

2.2.1 Fresh Meat Products

2.2.1.1 Color

Color is one of the first quality attributes consumers use when assessing meat because it is one of the first things they will notice when choosing meat to purchase (Troy &

Kerry, 2010). Discoloration is used by consumers as an indicator of freshness and, as a

7 result, discolored retail meat may be sold at a discounted price. This has resulted in upwards of $1 billion/year in revenue losses in fresh beef alone (Smith, Blek, Sofos,

Tatum, & Williams, 2000). In terms of color, consumers have learned to expect that the highest quality meat will be bright cherry-red for red meat and an even pink color for poultry and pork. Though color is not necessarily linked with the eating quality, it still has a significant impact on consumers’ intent to purchase (Carpenter, Cornforth, &

Whittier, 2001).

The color of fresh meat is determined by the protein myoglobin, which is a small, water-soluble oxygen binding protein held in the sarcoplasm of meat. The color of this protein, and in effect the perceived color of the meat, is determined by the oxidation state of the iron complexed at the center of the heme group held within the myoglobin protein

(Mancini & Hunt, 2005). The color of the forms of myoglobin typically found in meat are shown in Figure 2.3. When meat is first cut it appears purple due to the lack of oxygen inside of the muscle post-mortem. Once it is exposed to air, the deoxymyoglobin is oxygenated resulting in a bright red color in a process known as “bloom.” This is a reversible reaction as long as iron is in the reduced state

(Fe+2). During storage, it is possible for the iron to lose an electron, Figure 2.3. Flowchart of reactions leading to color changes in fresh meat. 8 resulting in the oxidized form (Fe+3) of myoglobin called metmyoglobin, which is a gray color. Meat also has inherent systems aimed at keeping the myoglobin in its reduced

(Fe2+) state referred to collectively as metmyoglobin reducing activity (Bekhit &

Faustman, 2005). These systems are complex and are impacted by many different factors within the meat itself as well as storage, processing, and other external factors.

2.2.1.2 Water Holding Capacity

Water-holding capacity (WHC) of meat is defined as the ability of the postmortem muscle (meat) to retain water even though external pressures (e.g. gravity, heating and freezing processes) are applied to it. The WHC of meat affects a number of perceived quality attributes including the appearance, cooking behavior, and ultimately the juiciness upon chewing (Lawrie & Ledward, 2006a). In processed meat products, the WHC affects the ability of the meat to uptake brine and retain it upon cooking, the mechanisms of which will be discussed in detail later. The WHC of meat can be impacted by a number of factors throughout the meat production chain, including the rearing of the animal, slaughter conditions, and downstream processing (den Hertog-Meischke, van Laack, &

Smulders, 1997). Here we will focus on post-slaughter conditions.

WHC is primarily determined by the status of the proteins within meat, primarily the proteins of the myofibrils, though sarcoplasmic proteins do play a role in the WHC of meat (Huff-Lonergan & Lonergan, 2005). The highly-organized structure of the myofibril is critical to ensuring the optimal WHC of meat. Some processes that disrupt this structure can cause a decrease in WHC but some changes, such as addition of salt and

9 phosphates, can increase the WHC. These changes include changes in pH, change in the ionic strength, denaturation and oxidation of the proteins, and proteolysis.

Drip loss is defined as the water that was previously held within the food that is then lost from the matrix and not resorbed into the system and is reflective of low WHC. This is a major quality defect in fresh meat and results in weight losses in the products which in turn leads to lower weights of fresh product sold to retailers and a corresponding loss of value. Excess drip is thought to be indicative of the inability of processed meats to hold the brine introduced during processing and cooking, which will be discussed more below.

2.2.1.3 Texture

Texture has been defined as “the sensory and functional manifestation of the structural, mechanical and surface properties of foods detected through the senses of vision, hearing, touch and kinesthetic” (Szczesniak, 2002). Texture and, specifically, tenderness of meat is named as the consumer’s most important quality attribute so much so that they may sacrifice color and flavor for improved textural properties; however these properties are some of the most difficult to define (Lawrie & Ledward, 2006a;

Stanley, 1983). Tenderness is defined as “the ease with which the meat yields on chewing and how much energy is required to masticate it to the state ready for swallowing”

(Szczesniak, 1998). The tenderness of meat is primarily determined by the different protein fractions in meat, namely connective tissue, myofibrillar proteins, and sarcoplasmic proteins. Many of the factors affecting meat tenderness are determined by the rearing and slaughter conditions of the animal including species and breed, age,

10 stress. These pre-slaughter factors primarily affect texture based on the amounts, distribution, and types of connective tissues within the muscle. The texture of meat is influenced by the properties of the connective tissue, mainly the amount, degree of cross- linking, and spatial orientation (Stanley, 1983). The contributions of connective tissue to texture are not impacted by post-mortem ageing and are directly related to the age of the animal at slaughter. Additionally, the state of the myofibrillar proteins can have a significant impact on meat texture. The length of the sarcomere has been directly associated with the texture of meat, with shorter sarcomeres resulting in tougher meat.

After slaughter, there are also a number of factors that can affect the texture of meat.

The contraction of the sarcomere during rigor mortis is one of the primary causes of the toughening of meat and changes to the rate of rigor completion can have a major impact on final texture. Metabolic events in the muscle as it is converted to meat during rigor results in a pH fall; a slower pH fall is associated with more tender meat. The handling of the carcass can have significant impact on the tenderness. If the muscle is cooled too rapidly (before the onset of rigor mortis), a contraction of the sarcomere called “cold shortening” occurs resulting in very tough meat (Bailey, 1972). The degree of proteolysis also has a significant impact on texture. The primary system responsible for meat tenderization is thought to be the calpain system, which functions by degradation of the

Z-line and the I-band of the sarcomere (Nowak, 2011). In short, the texture of meat is extremely complex and is determined by numerous factors. The primary factor, however, is the physical state of the proteins within the meat and by optimizing the other factors, the texture of meat can be modified and optimized for eating quality.

2.2.1.4 Other quality attributes

Though this work is focused on the physical quality of meat, it is important to acknowledge the microbial and chemical aspects of meat quality.

Microbial quality of meat can be divided into two main concerns: spoilage and safety.

Spoilage of meat can occur by a combination of different microorganisms, depending on the temperature and moisture conditions. Because meat is typically stored under chilled and moist conditions, only psychrotophic microorganisms will grow, which represent a small fraction of the microbes present (James & James, 2002). The major contributors are

Gram negative bacteria, such as Pseudomoas (Ingram & Dainty, 1971). The proliferation of these bacteria result in off odors as well as a tackiness on the surface of the meat. As the growth of these microorganisms continues, a slime may be formed on the surface and the putrid smell becomes increasingly evident. In frozen meat, the activity of bacteria is effectively stopped, however, some molds are able to grow at temperatures as low as –10 or –12°C, though practically the minimum temperature for mold growth is about –5°C

(James & James, 2002).

The prevention of the growth of pathogenic microorganisms on meat is important to maintaining public health. Pathogens of concern in fresh meat include Salmonella spp., enterohaemorrhagic Escherichia coli (including O157:H7), Campylobacter, as well as other enteric pathogens (Sofos, 2008). Listeria monocytogenes is also of significant concern, particularly in ready-to-eat meat and poultry products, because the organism can grow at refrigeration temperatures. The primary means of maintaining safe meat is to

12 follow appropriate processing, handling, and cooking procedures to prevent contamination and eliminate potential pathogens.

The flavor and aroma of meat is determined by the interplay of hundreds of different compounds, many of which are altered during storage and cooking (Calkins & Hodgen,

2007). In terms of off-flavors and odors that can occur during storage or processing of meat, the main issues of concern are lipid oxidation and microbial activity (as discussed earlier) (Gray, Pearson, & Monahan, 1994). The stability of lipids depends on factors such as temperature, presence of antioxidants, presence of catalysts (such as iron), and oxygen level. By minimizing lipid oxidation, the off-odor can be minimized and, consequently, acceptability of meat can be increased.

2.2.2 Processed Meat Products

In addition to fresh meat products, over 20% of meat is used to create processed meat products including lunch meats, sausages, hams, etc. (Daniel, Cross, Koebnick, & Sinha,

2011). The same quality attributes that we are concerned about in fresh meat products are of concern in processed meat products since the quality of a processed meat product is dependent on the quality of the fresh meat inputs; however there are some particular considerations for processed meat products that vary from those in fresh meat products.

2.2.2.1 Cook loss and water holding

One of the most prevalent meat quality issues is unacceptably high moisture loss, or low WHC, called cook loss in processed products. Meat with low WHC often tends to produce inferior processed products. Cooking causes structural changes in the proteins

(denaturation) which reduces the WHC of meat (Tornberg, 2005). In whole meat, the

13 change in WHC is primarily due to loss of the initial structure of muscle, shrinkage of the muscle fibers, and a subsequent decrease in space for water to be held. In contrast, for processed meats, such as hams, deli meats and bratwursts, the water holding is due to the generation of a thermally induced gel. If the myofibrillar proteins are compromised during pre-processing activities, the gel will not be able to entrap as much water upon heating, causing an increased cook loss and lower quality final cooked product.

The addition of functional non-meat ingredients in processed meat products aim to enhance the eating quality, the most widely used of which are sodium chloride (NaCl) and phosphates, and safety (nitrite and NaCl) of meat (Petracci, Bianchi, Mudalal, &

Cavani, 2013). The addition of NaCl to meat systems has multiple functions including the improvement of texture and WHC as well as enhancing the flavor. The mechanism of action is through the adsorption of Cl− ions to positively-charges groups on myosin causing a charge repulsion, allowing the filaments to separate and hold more water.

Phosphates function in multiple ways to improve the functionality of meat proteins: 1) sequestering calcium ions, which weakens the actomyosin complex; 2) raising of the pH

(away from the isoelectric point); 3) increasing electrostatic repulsive forces; and 4) increasing the ionic strength which results in increased swelling. These modifications to the interactions of the meat proteins allows swelling of the myofibril allowing the uptake and immobilization of water. Phosphates also protect the meat from lipid oxidation primarily by sequestration of ions (Shahidi, Rubin, & Wood, 1987). Other ingredients such as hydrocolloids, starches, and exogenous proteins may also be added to increase the functionality of processed meat products in various ways.

2.2.2.2 Thermally-induced meat gel formation

As alluded to in the previous section, the ability of meat proteins to form a gel is a significant quality parameter in many processed meat products (Tornberg, 2005). This is often aided by the addition of functional ingredients such as salts, phosphates, hydrocolloids, or other ingredients (Petracci et al., 2013). Gel formation is dictated primarily by the myofibrillar proteins, of which, myosin is the predominate contributor

(Macfarlane, Schmidt, & Turner, 1977). Changes in myosin solubility during storage at low ionic strength has been shown to reduce the gel forming ability due to aggregation of the proteins (Ishioroshi, Samejima, & Yasui, 1979). Similarly, Tornberg, Andersson, &

Josell (1997) showed that in an emulsion sausage, if the proteins were aggregated the stress required to break the gel was reduced and the resulting product was found to be brittle and grainy, which caused it to be less preferred by a sensory panel. Thus, aggregation of the proteins prior to processing could result in reduced quality.

2.3 Measurement of Meat Quality

In order to produce the highest quality meat products, it is important to determine the best methods for measuring meat quality. There are different methods to measure the various quality attributes of meat, mentioned previously. Typical methods used in the analysis of meat quality will be discussed as well as advanced methods used to understand the mechanisms behind the observed changes in meat quality.

2.3.1 Typical Methods

2.3.1.1 Color

The color of meat can be measured a number of ways including instrumental measurements, computer vision, spectrally, and even visually (Mancini & Hunt, 2005).

The Hunter L*, a*, b* color system is one of the most commonly used means of describing the color of foods. This is measured using a colorimeter, which uses a light source and a detector to measure the light reflected off of a sample. The instrument gives you the lightness (L*) value on a scale from 0 (black) to 100 (white) as well as the a* value, on a scale from green (negative values) to red (positive values) and the b* value, on a scale from blue (negative values) to yellow (positive values).

Spectral measurements of color can be performed by surface measurements on whole meat (American Meat Science Association, 2012) but have also been useful in the exploration of myoglobin chemistry in solution (Allen & Cornforth, 2006; Gotoh &

Shikama, 1974; Zachariah & Satterlee, 1973). These measurements are most often used to determine the relative proportions of the various forms of myoglobin, mentioned previously. The current preferred conversion from spectral data to myoglobin forms is the set of equations outlined by Tang et al. (2004).

2.3.1.2 Water Holding Capacity

The measurement of water holding capacity (WHC) is an important because it is directly related to the juiciness of the processed meat product. There exists a multitude of methods to measure WHC with little consensus about which to use among industry and researchers alike (Hamm, 1960; Honikel, 1998; Trout, 1988). The primary methods for

16 measuring WHC include: gravitational (drip loss), press, and centrifugal methods. With each method, the quantity of WHC varies depending on the method of measurement and the external force applied.

Different forms of gravitational drip loss measurement are used in the meat industry.

The most common and simplest method is that developed by Honikel (1998) in which a meat sample is suspended in a plastic bag. Weight of the sample is recorded before the beginning of the test and then again after 24-48 h of storage at 1-4°C. The difference between the initial and final weights is divided by the initial weight to determine the percentage of drip loss. Various modification on this method have been made to accommodate different sample dimensions and hanging methods, but the principle remains the same.

A drip tube method (call “EZ-DripLoss”) has more recently been developed as an alternative to the bag method, particularly in pork (Rasmussen & Andersson, 1996). This method uses specially designed tubes which hold a 25-mm diameter, 2.54-cm thick core sample in an upper well and allow the drip to drain into a lower tube. As with the bag method, the samples are weighed before and after holding for a set time (48 h at 4°C) to determine drip loss.

The press method is performed by placing a sample of meat on a piece of filter paper

(or between two pieces of filter paper) and applying a pressure causing the sample to form a thin film (Hamm, 1960). The water that is not held by the meat is expressed and absorbed by the filter paper, causing two rings to form: one of meat and the other of the expressed water. A linear association of the amount of expressible water and the area of

17 wetted paper is used to calculate the amount of free or expressible water (Apple &

Yancey, 2013).

Methods of centrifugation have also been used to determine WHC, though the parameters vary. Some researchers utilized high speeds for long times (10,000g for 1 h)

(Bouton, Harris, & Shorthose, 1971) while others found reproducible results at lower speeds and shorter times with special inserts for centrifuge tubes (Penny, 1975). Both of these methods express the drip from the meat sample with the force generated through centrifugation. Other methods of centrifugation require the addition of water or brine

(Barbut, 1996; Hamm, 1960; Updike et al., 2005).

2.3.1.3 Cook Loss

Similar to drip loss described above, cook loss is determined as the mass difference before and after cooking. One standard method is described by Honikel (1998) in which samples no more than 50 mm thick are weighed and placed into plastic bags and submerged in a boiling water bath until reaching a final temperature of 75°C. Samples are then submerged in a cooling bath at 1-5°C prior to blotting and re-weighing. Cook loss is determined as the weight lost as a percentage of the initial sample weight. Generally speaking, a cook loss method will have a defined heating and cooling protocol which matches as closely as possible the cooking expected for the product. Cook loss amount can vary with the time and temperature used to perform the cook (Tornberg, 2005).

2.3.1.4 Texture

Texture is one of the most complex aspects of food quality, but also one of the most important to the consumer experience. The traditional method of measuring the textural

18 quality of foods was done through sensory evaluation by a trained or untrained human panel, depending on the desired outcomes (Chen & Opara, 2013). However, the use of sensory panels can be expensive, requires a large time investment, and is subject to inherent human error. Due to these limitations, a large effort has been put forth to develop objective instrumental measurements that can be used to assess the texture of food products. Figure 2.4 shows three typical probes used in the analysis of meat texture, each of which will be discussed in more detail below.

The most widely used instrumental measure for meat tenderness is the Warner-

Bratzler shear (WBS) (Figure 2.4A) force test (Damez & Clerjon, 2013). This method involves a blade of exact specifications with a V-shaped notch in it. Samples must be prepared as 0.5 inch (1.27 cm) cores parallel to the longitudinal fiber direction and typically 6 core samples are required for each sample analyzed. The WBS method, though the current standard, is not without its flaws. This method has been shown to have high correlation with sensory data in beef steaks (Shackelford, Wheeler, & Koohmaraie,

1995); however, this method may not be appropriate for all types and cuts of meat. It also requires a lot of precise sample preparation, which can lead to long analysis Figure 2.4. Probes used in typical meat texture analysis methods: (A) times and Warner-Bratzler, (B) Allo-Kramer, and (C) Blunt Meullenet-Owens Razor shear. (Photos from Texture Technologies Corp.) difficulty in

19 reproducibility.

Improvements have been attempted by refining the WBS method including the Allo-

Kramer (AK) shear test (Figure 2.4B). The Kramer shear press is used to first compress and then shear a sample (Dodge & Stadelman, 1959). The addition of multiple blades is meant to help minimize variation from the product as the shear is performed. Though this provided an improvement on the WBS’s single blade method, the AK shear has its own limitations including dependence on sample weight and thickness, presence of connective tissue, and dehydration (Dodge & Stadelman, 1959; Smith, D. P., Lyon, C. E., Fletcher,

1988).

Due to difficulty in preparing consistent samples for the analysis of poultry products, other methods have been developed including the Meullenet-Owens Razor Shear

(MORS) (Figure 2.4C) method (Lee, Owens, & Meullenet, 2008). This method is more appropriate for poultry samples due to the difficulty in preparing samples with muscle fibers in the same direction as is required in the WBS test. In the MORS method, a razor blade of defined dimensions is inserted directly into the poultry meat sample 20 mm without any sample preparation. These two factors (no sample preparation and small deformation) allow for much more rapid and reproducible measurements. Outputs from the MORS test include the razor blade shear energy (calculated from the area under the force deformation curve) and the maximum shear force (the maximum force measured throughout the test). These two parameters are used to determine poultry tenderness with four measurements per fillet.

2.3.2 Advanced Methods

2.3.2.1 Thermal Analysis

Since the works of Slade and Levine (1988, 1991), food scientists have begun examining foods from a materials science perspective. This has led to a growing body of research using thermal analysis methods, originally designed for analysis of polymers, in food applications. A number of techniques fall under the category of thermal analysis, all having a similar basis for their application which is that changes in the physical properties of a sample are measured with respect to temperature. Though many techniques exist and have been used within explorations in food, the primary thermal methods used in meat analysis have been limited to differential scanning calorimetry

(DSC) and thermogravimetric analysis (TGA) (Tamilmani & Pandey, 2016).

DSC is a method whereby a small amount of a sample (10-15 mg) is placed into a sample pan and taken through a designated thermal cycle. Throughout the thermal cycle, the heat flow through the sample is compared to that through a reference

(typically empty) pan. The difference in the heat flow gives an output of the heat flow through the sample as a function of Figure 2.5. Idealized thermogram for temperature, referred to as a thermogram meat showing the three primary transitions: (I) myosin, (II) sarcoplasmic (Thomas & Schmidt, 2010). An idealized proteins and collagen, and (III) actin. (Tamilmani & Pandey, 2016) thermogram for a meat sample in the

21 temperature range of protein denaturation is shown in Figure 2.5. In this example, three endothermic peaks can be seen, which have been identified to correspond to different proteins present in meat which denature at different temperatures: myosin (54-58°C), sarcoplasmic proteins and collagen (65-67°C), and actin (71-83°C) (Findlay, Parkin, &

Stanley, 1986).

TGA is a method of thermal analysis that measures the weight change of a sample as a function of temperature (Thomas & Schmidt, 2010). Again, a small sample (10-20 mg) is placed in specially-designed pans which are held on an extremely sensitive balance. A furnace is placed around the sample which heats the sample at a specified rate to drive off different componenets. This can be used to look at bulk moisture, “bound” water, and decomposition temperature. In addition to the mass loss, the derivative curve (DTG) of the weight loss is also plotted; this allows for better identification of transitions throughout the thermal cycle. This technique has not been used as much in meat, but has been used extensively in understanding the water binding properties of bread dough and its ingredients (Fessas & Schiraldi, 2001; Simmons, Smith, & Vodovotz, 2012). Figure

2.6 shows example DTG curves in doughs with and without soy and the ingredients used in these doughs. The different proteins in the doughs (soy protein and gluten) show quite different water binding behavior. This method may be used to get a better understanding of how changes in meat structure affect the water-binding capabilities of the system.

Figure 2.6 Example DTG curves of soy and wheat bread doughs (a) and the raw ingredients used to make these doughs (b). (Simmons et al., 2012)

Thermal analysis has been used to study a number of facets of meat production and is covered extensively in a recent review (Tamilmani & Pandey, 2016). For example,

Kijowski & Mast (1988) used DSC to show that phosphates increased the thermal stability of myosin while salt decreased the thermal stability of actin in chicken meat.

DSC has also been used to assess changes in the free and bound water in beef muscle

(Aktaş, Tülek, & Gökalp, 1997). DSC can be used in conjunction with other methods such as nuclear magnetic resonance (NMR) to determine the sorption properties of meat proteins (Venturi et al., 2007). These are just a few of the many potential applications for thermal analysis in the understanding of meat quality.

Methods such as DSC and TGA can be used to get a better understanding of how the water and proteins change as a function of different processing and storage conditions.

By coupling these types of analyses with functional analyses, we can get a better understanding of what molecular changes are occuring within meat during processing;

23 this allows us to optimize meat quality based on an understanding of the molecular interactions involved.

2.3.2.2 Water Mobility

Macroscopic observations, like drip loss, are evidence of microscopic phenomena.

Therefore changes in the microscopic spatial distribution of water can have major effects on meat quality. Modern techniques such as NMR can be used to monitor changes in water mobility (Kerr, 2008). Using NMR to measure the spin-lattice (T1) and spin-spin

1 (T2) relaxation times for the water protons ( H), it is possible to assess the state and mobility of the water within a food matrix (Reuhs & Simsek, 2010). If we can relate the macroscopic defect of drip loss to microscopic measurements from NMR we can elucidate the mechanisms involved in drip loss from thawed meat. Similar techniques may be used to determine differences in water mobility based on frozen storage times and temperatures. This technique has been used in food systems before to assess water mobility.

Low-field NMR (LF-NMR) has been used previously to determine the different populations of water in meat (Hanne Christine Bertram, Purslow, & Andersen, 2002;

Damez & Clerjon, 2013; Venturi, 2007). These studies have shown that it is possible to differentiate bound, interstitial (physically entrapped), and free (unbound) water within the structure of meat with T2 values corresponding to 1-10 ms, 40-60 ms, and 150-400 ms, respectively. The majority of the water within the muscle protein structure is represented by the interstitial water. Evidence suggests that changes in this so-called

“boundness” of water has major impacts on meat quality and is strongly impacted by freezing and frozen storage conditions (Leygonie, Britz, & Hoffman, 2012).

Other NMR techniques exist, though they have yet to be explored in meat systems.

One such method is called cross-relaxation or “Z-spectroscopy.” This method probes the polymers within a system by observing the 1H spectrum of the water, which is magnetically coupled to solid-like components (Wu, Bryant, & Eads, 1992). This allows the examination of the mobility of polymers within a system without using a solid-state

NMR method. The pulse sequence requires a Gaussian pulse followed by a 90° pulse.

The pulse is repeated at different off-set frequencies from that of the 1H nuclei, resulting in a spectrum representative of the solid component’s mobility. This method has been primarily used to look at starch (Wu et al., 1992) as well as bread and its components

(Lodi, Tiziani, & Vodovotz, 2007; Vodovotz, Vittadini, & Sachleben, 2002). These studies have been successful in relating quality changes in these products and ingredients to changes in the molecular mobility of the polymer components. Further work using cross-relaxation in other food products with a significant dependence on the state of the polymers could help elucidate mechanisms of quality loss and provide a better understanding of how to minimize these losses.

2.3.2.3 Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a technique similar to NMR in that the relaxation of nuclei in a magnetic field is measured but with the added benefit of the construction of an image from these signals throughout a sample of interest. MRI is most commonly utilized in the health field to image human subjects for the diagnosis of

25 diseases. The application of MRI to food systems is relatively new but could provide a large amount of structural information within food products of interest. In relation to freezing, MRI has been explored as a method of authentication for products which claim to have not been frozen. For instance Guiheneuf et al. (1997) were able to differentiate fresh from frozen and thawed pork based on transverse relaxation time (T2) and apparent magnetization transfer rate. MRI has also been used to study frozen fish and shown that freezing and thawing has a significant effect on the apparent perpendicular diffusion coefficient (D⊥) and the T2 mean value (Foucat, Taylor, Labas, Renou, & Services, 2001).

The change in T2 is consistent with protein denaturation and results from changes in the distribution and compartmentalization of water due to gaps in the muscle fibers created by ice crystals (Renou, Foucat, & Bonny, 2003). The application of diffusion tensor imaging (DTI) has been used to explore how anisotropic diffusion within the highly organized muscle system can be used to understand structural changes (Hanne Christine

Bertram & Andersen, 2004). Bonny & Renou (2002) used DTI to examine two different muscle types in beef and were able to use different parameters (mean diffusivity, anisotropy, orientation and T2 weighting) to assess the localization of free water.

A few studies have used NMR micro-imaging to study the characteristics of water in muscle foods based on different treatments. One study examined the formation of drip channels in rabbit muscle post-mortem (Hanne Christine Bertram, Whittaker, Andersen,

& Karlsson, 2004) and were able to identify locations where free water accumulated

(Figure 2.7). The same group used micro-imaging to examine beef as a function of ageing and high-pressure treatment (Hanne Christine Bertram, Whittaker, Shorthose,

Andersen, & Karlsson, 2004). In this study NMR was used to determine the distribution of T2 values which showed a high correlation with cooking loss. Both of these studies examined the T2 maps exclusively, which corresponds with the larger body of research which shows good correlations between T2 measurements and various meat quality parameters, particularly those influenced by the state of water. These were also fairly low resolution studies with 15 × 15 mm field of view and 3 mm slice thickness.

Advances in NMR micro-imaging have allowed for higher-resolution at a smaller scale with the potential to image with a 10 Figure 2.7. T2 map of rabbit muscle (a) μm or less resolution using higher powered 45 min, (b) 105 min, (c) 225 min, and (d) 345 min post-mortem. Arrows indicate magnets which could allow a deeper localization of high-mobility water withing the muscle sytem. (Bertram, et understanding of the effects of processing al., 2004) on the state of water within a food system on a microscopic scale.

2.3.2.4 Rheology

Rheology allows for the analysis of the viscoelastic properties of food samples by measuring the stress and strain response to various deformations. In contrast to textural measurements, rheology uses small deformations that do not typically result in destruction of the sample, unless that is the point of the measurement. The storage and loss moduli (G’ and G’’) are monitored to determine changes in the matrix structure

(Daubert & Foegeding, 2010). In meat products, there is a particular interest in the response of products to the application of temperature as proteins denature and form gels upon heating; differences in these gel-forming properties can have significant impact on processed meat product final quality (Tamilmani & Pandey, 2016).

Rheology has been used, oftentimes in conjunction with thermal analysis, to explore the effects of addition of different proteins (Doerscher, Briggs, & Lonergan, 2004), pH

(Westphalen, Briggs, & Lonergan, 2005), and addition of various hydrocolloids

(Chattong, Apichartsrangkoon, & Bell, 2007; DeFreitas, Sebranek, Olson, & Carr, 1997;

Funami, Yada, & Nakao, 1998). The roles of different meat protein roles in meat gel formation have been explored using rheology to better understand the mechanism behind this phenomenon (Samejima, Ishioroshi, & Yasui, 1981; Sano, Noguchi, & Tsuchiya,

1988). This technique has also been used to explore the quality of turkey breast meat and determine the proteins involved in gelation beyond actin and myosin (Updike et al., 2005,

2006).

2.3.2.5 Gel electrophoresis

Gel electrophoresis is an analytical technique used to assess the protein composition of meat. Different types of gel electrophoresis can be used with different conditions including native vs. denaturing, reducing vs. non-reducing, and two-dimensional gels.

The majority of studies in meat use sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Rosenberg, 2005). The basic concept is based on the differences in molecular size of proteins in a sample and the effect on migration in a gel exposed to an electric field. In SDS-PAGE, proteins are denatured and coated in

28 negatively-charged SDS. This eliminates the effect of the inherent charge densities of individual proteins in the electrophoresis buffer conditions and shape on the migration on the proteins. When the electric field is applied, the proteins migrate toward the cathode

(positive charge). A molecular weight (MW) ladder is loaded in one or more of the lanes when running gels to provide reference and create a standard curve of MW vs. distance traveled by the individual proteins/peptides in the gel. By knowing the possible proteins in the sample and their MW, it is possible to identify and sometimes quantify the different proteins in the system using image analysis.

SDS-PAGE has been used in countless studies of meat quality including determination of the role of sarcoplasmic proteins in WHC of pork (Joo, Kauffman, Kim,

& Park, 1999), determination of protein indicators of pork ageing in drip (Di Luca,

Mullen, Elia, Davey, & Hamill, 2011), and determination of proteins associated with low functionality turkey genetic lines (Updike et al., 2006). This versatile method can be used in conjunction with any and all of the methods described here to determine the role of various protein fractions in different functional capacities.

2.3.2.6 Microscopy

Different types of microscopy have been used for decades to directly observe the structural characteristics of meat with the goal of understanding the effects of these structures on quality (Stanley, 1983). Figure 2.8 shows how scanning electron microscopy (SEM) and transmission electron microscopy (TEM) can be used to assess the effect of freezing rate on pork structure (Yu et al., 2010). Microscopy can be used not only for qualitative assessment, but also quantitative measures of meat quality.

One quantitative measurement deduced from microscopy is the myofibrillar fragmentation index (MFI), which is an indicator of the degree of myofibril breakdown and is correlated with tenderness

(Davey & Gilbert, 1969; Hay,

Currie, Wolfe, & Sanders, 1972; Figure 2.8. SEM (A & B) and TEM (C & D) images from fast (A & C) and slow (B & D) frozen Sayre, 1970). Some authors pork (Yu et al., 2010) noted the increase in light absorbance or scattering with increased fragmentation, which led to the development of a method to determine MFI by absorbance at 540 nm in a spectrophotometer (Olson, Parrish, & Stromer, 1976; Veiseth, Shackelford, Wheeler, &

Koohmaraie, 2001).

Sarcomere length is another important structural measurement that can be an indicator of meat quality that can be determined by microscopy. Shortened sarcomeres have been shown to correlate with decreased WHC and a lowered T21 population (which corresponds to less entrapped water) (H C Bertram et al., 2001; Hanne Christine Bertram et al., 2002). The shortening of the sarcomere results in a decrease in the volume, meaning less water can be held, and an increase in protein density, meaning increased resistance (toughness); indeed, meat processors have taken advantage of this finding by

30 manipulating the pH and osmotic forces in meat to induce and enhance myofibrillar swelling (Puolanne & Halonen, 2010).

2.4 Influence of Freezing on Meat Quality

The functionality of meat under frozen storage conditions is known to be substantially reduced (Miller, Ackerman, & Palumbo, 1980). This loss of functionality is due to a combination of mechanisms including: mechanical damage, protein dehydration, concentration of solutes, and protein oxidation (Xiong, 1997). Quality losses in frozen meat include: increased drip loss, increased cook loss, and inferior texture. It has also been observed that during the process of freezing, frozen storage and thawing that the protein structure protecting the heme group of myoglobin may be denatured (Calvelo,

1981). This has been shown to cause an increased susceptibility of myoglobin to oxidation resulting in increased color degradation in previously frozen meat (Leygonie et al., 2012). Clearly, the process of freezing can cause significant loss of meat functionality, and subsequently loss of quality. It is important to understand how the freezing process influences the state of water within meat and meat products in order to design freezing processes that minimize energy input while maximizing quality.

2.4.1 Mechanisms of Water Holding in Meat

Water within meat is held by the combination of mechanisms, primarily electrostatic interactions, osmotic forces, and capillary action within the myofibrillar (structural) proteins, namely myosin and actin; water-soluble (sarcoplasmic) proteins do play a role in water holding, though primarily because of interactions with the structural proteins

(Hamm, 1960; Puolanne & Halonen, 2010). This results in a gradient of water holding

31 within meat, with no well-defined delineation between strongly- and loosely-bound water. Only a small fraction (about 10%) is considered the “true hydration water” of the meat proteins, meaning that the remainder is technically “free water,” though it may be entrapped within myofibrils or weakly associated with the bound water. The terms “free” and “bound” water are considered outdated terms that over-simplify the complex interactions between polymers (like proteins) and water (Slade & Levine, 1991). NMR has been used to show that the mobility of water, even water thought to be integral to the structure of a polymer, have a high degree of mobility and freely exchange with other water in the system. This complicates our understanding of WHC in meat, though through modern analysis techniques we can hope to increase our knowledge of the mechanisms controlling how freezing changes the structure and function of proteins within a meat system.

Changes in myofibrils, such as cross-linking and denaturation, can change capillary action as well as the osmotic swelling of the myofibrils resulting in a lowered WHC

(Puolanne & Halonen, 2010). As mentioned earlier, freezing can cause the induction of these changes and the myofibrillar proteins are particularly susceptible to freeze denaturation (Xiong, 1997). Figure 2.9 illustrates a simplified model of how freezing can result in lowered WHC. This cold-induced denaturation is due to the thermodynamics at play in protein-water and protein-protein interactions as governed by the Gibbs’ free energy equation:

∆퐺 = ∆퐻 − 푇∆푆 (1) where ΔG is the free energy, ΔH is the enthalpy and ΔS is the entropy (Privalov &

Makhatadze, 1993). In the native state, the entropy increase in water favors the folding of the protein, even though it is not entropically favored, by concealing hydrophobic groups inside the protein, resulting in a net decrease in energy (ΔG). However as the water freezes it forms crystals, which decreases the entropy of the system. As a result, the polar interactions with the protein are disrupted causing an increase in the role of hydrophobic interactions which can cause hydrophobic side chains to become exposed and increasing protein-protein interactions and causing changes to protein conformation and, maybe, aggregate. This denaturation lowers the ability of the proteins to hold water, likely through the reduction in capillarity, after thawing and can have a significant impact on final product quality.

2.4.2 Freezing Rate

When freezing was first introduced as a food preservation technology, the importance of the freezing rate, or, how fast ice forms, was thought to be the primary factor affecting frozen food quality due to the size of the ice crystals formed under various rates (Reid,

1997). The focus of industry practice has been on freezing foods as rapidly as possible to ensure the formation of small ice crystals within the product structure. These steps are thought to ensure the reduction of cellular damage and lessen the impact on the texture of product after freezing. It has been demonstrated that a product which is frozen and immediately thawed can be indistinguishable from the fresh product (Jul, 1984).

However, contradictory results are presented in the body of published literature on the subject. For instance, studies on the formation of drip loss have been found by a number of authors to be decreased with increased freezing rate (Li, Heaton, & Marion, 1969;

Moran, 1932; Ramsbottom & Koonz, 1939; Sacks, Casey, Boshof, & Zyl, 1993; Yu et al., 2010) while another group of authors have found no correlation (Crigler & Dawson,

1968; Empey, 1933; S. James, Nair, & Bailey, 1984; Love, 1957) and others have found unique, non-linear relationships (Añón & Calvelo, 1980; Petrović, Grujić, & Petrović,

1993). Clearly, the impact of freezing rate is not as straightforward as once thought.

Using DSC, Wagner & Añon (2007) were able to determine that freezing rate had an effect on the myosin head region in particular, which was confirmed with ATPase activity measurements. It was noted also that myosin was denatured regardless of freezing rate, but differences were greater at slower freezing rates. At fast freezing rates

(less than 5 min freeze time) there was a 3.27% reduction in total enthalpy as opposed to

8.17 and 9.07% loss at intermediate (20-25 min freeze time) or slow (greater than 60 min freeze time) freezing rates, respectively. It is also important to consider the limitations to freezing rate in meat systems since, in many cases, fast freezing rates are not feasible due to product dimensions (Xiong, 1997).

2.4.2.1 Ice crystal size

The size and location of ice crystals formed during freezing of meat can be impacted by the direction of the freezing front relative to the direction of the muscle fibers (A. E.

Bevilacqua & Zaritzky, 1980; A. Bevilacqua, Zaritzky, & Calvelo, 1979). These studies showed that intracellular ice forms only in cases of fast freezing (short freeze times) and that these times must be even shorter when the freezing front is perpendicular to the fiber direction. These studies also established that a relationship between freezing time and average ice crystal diameter that indicates that ice crystal size is dependent on freezing time in an exponential manner (Eqn. 2).

퐷 = 푎 + 푏 ln (푡푐) (2)

Where D is the average ice crystal diameter, tc is the characteristic freeze time, and a and b are experimental constants. Slower freeze times result in larger ice crystals and, as a result, increase the amount of cellular damage.

Formation of drip is also due to relocation of water within the muscle structures.

According to Pham & Mawson (1997), 5-12% of the total water in meat between muscle fibers (intercellular) and the remainder is held within the muscle cells (intracellular).

Within the muscle fibers, the majority (ca. 70%) is held within the myofibrils with the remaining 30% held in the sarcoplasm. Freezing can cause a migration of water from its previously held state to a more free state, resulting in an inability of the protein to resorb the water upon thawing causing drip loss. Larger ice crystals have a greater surface energy than small ice crystals which could cause a greater force in the relocation of water within the myofibril (Xiong, 1997).

2.4.3 Frozen Storage Temperature

Even if meat is frozen ideally and results in minimal quality loss, changes in the quality of the same product may be detected after several months of storage at constant temperature. In some situations, changes in quality may be detected after weeks of storage, emphasizing the role of storage temperature in maintaining frozen food quality.

The recommended temperature for frozen storage established in the 1950s is −18°C (0°F) and is still the current recommended storage temperature for frozen food (Frozen Food

Handling and Merchandising Alliance, 2009). This temperature was found to keep a majority of food products fresh for up to one year.

2.4.3.1 Reaction Kinetics

The influence of storage temperatures on food product quality attributes (drip loss, color, etc.) are traditionally fit to the Arrhenius model. In the most general form, the rate equation is:

푑푄 − = 푘푄푛 (3) 푑푇 where Q is the magnitude of the quality attribute, k is the rate constant and n is the order of the reaction. Most published data in the area of food quality analysis are fit to a first- order reaction, and provide a first-order rate constant (k) as a function of temperature.

The influence of temperature on the rate constants is fit to the Arrhenius equation:

푘 = 푘푟exp [−퐸퐴⁄푅푇] (4) where kr is the rate constant at a reference temperature, EA is the activation energy constant and T is absolute temperature. Based on a dataset of reaction rates, the EA constant can be determined. These values provide a quantitative indicator of the

36 sensitivity of the quality attributes to storage temperature. The quantitative indicator (EA) is critical in evaluating the impact of storage temperature on food quality.

2.4.3.2 Reverse Stability

In some products, certain reactions have been observed to have an increased rate in the frozen state. A number of these reactions are summarized by Fennema (1973) and include various hydrolysis, insolubilization and oxidation reactions which can cause a deterioration of food quality. This phenomenon, termed ‘reverse stability’ is thought to be a product of the freeze-concentration effect, which is caused by a concentration of solutes in the unfrozen water phase as ice forms crystals of pure water within the food matrix

(Figure 2.10). The increased concentration results in a higher statistical probability for a reaction to occur resulting in a higher rate than would normally be anticipated by

Figure 2.10. Illustration of freeze-concentration. The dark blue represents the unfrozen water fraction, light blue represents ice, and red circles represent dissolved solutes. traditional Arrhenius kinetics, which dictates that a decrease in temperature results in an exponential decrease in reaction rate. One reaction that follows this trend is the oxidation of oxymyoglobin to metmyoglobin, the reaction that results in the browning of raw and cooked meat. This reaction has been found to have a maximum rate around −15°C

(Brown & Dolev, 1963). Other research suggests the rate maximum is closer to temperatures just below the freezing point (−2°C to −3°C) (Lai, 1984).

In addition to the increased probability of reactants interacting, protein-based systems are also susceptible to denaturation by the freeze-concentration of salts in the aqueous

37 fraction of a food product, as mentioned previously. As freezing progresses, the amount of salt in the unfrozen fraction increases exponentially. The high salt concentration causes a disruption of the electrostatic forces responsible for maintaining the native tertiary and quaternary structure (Xiong, 1997). This can lead to denaturation, dissociation of subunits, and aggregation of the protein molecules.

The reaction of malondialdehyde with myosin has been observed to increase significantly upon freezing, indicating denaturation of myosin that is an indication of loss of meat protein functionality (Buttkus, 1967; Lindeløv, 1978). Buttkus (1970) analyzed the denaturation of myosin in a high-salt solution and found that above freezing increased salt did not result in increased denaturation, but denaturation was maximized at −10°C.

The of functionality of meat depends on the downstream application but includes various measurable factors including drip loss, water holding capacity, thermally induced meat gel formation, and texture among others mentioned previously. It is important to understand the possible contributions of reverse stability to the loss of quality in frozen foods, especially when attempting to model quality loss.

2.4.3.3 Ice crystal growth

Ice crystal size can have a significant impact on frozen food quality (Petzold &

Aguilera, 2009). Ice crystal size and location is typically dictated by the rate of freezing with faster freezing resulting in smaller ice crystals, evenly distributed throughout the product as mentioned earlier. However, ice crystals can melt and grow throughout frozen storage (Fennema, 1973). Small crystals, though ideal for quality, are also more susceptible to melting due to their size. When ice crystals melt, thermodynamics dictates

38 that the most likely scenario is that the newly liquid water will redeposit onto the surface of existing crystals rather than reform the original crystal. This phenomenon is called

Ostwald ripening and occurs in other types of crystals as well (LeMeste, Champion,

Roudaut, Blond, & Simatos, 2002). This results in large crystals (which are more resistant to melting) growing at the expense of small crystals, resulting in a loss of quality. Due to these recrystallization phenomena it is possible to start with a product frozen under ideal conditions, but after storage the quality is lost due to an increase in the size of the ice crystals (Pham & Mawson, 1997).

During the storage of frozen beef, Bevilacqua & Zaritzky (1982) found that the rate of recrystallization of ice followed first-order

Arrhenius behavior, meaning that the rate of recrystallization increased exponentially with temperature.

These authors determined the activation energy (Ea) Figure 2.11. Effect of initial diameter (Do) and temperature on time to reach D1 (60 µm) in beef semitendinosus (From was 43.5 kJ/mol. Zaritzky & Martino, 1988).

Zaritzky & Martino (1988) found consistent data with the previously mentioned study (Ea

= 42.37 kJ/mol). In addition, these authors determined that the limit ice diameter (D1,

39 maximum ice crystal size) could be determined in meat using the muscle fiber dimensions and calculated the value to be about 61 µm in beef. The time to reach D1 was dependent upon temperature and initial ice crystal diameter (Do), as illustrated in Figure

2.11.

2.5 Conclusion

The quality of meat is based on a complex system of physical, chemical, and microbial attributes. In trying to understand the effect of various processing and storage parameters, such as freezing and frozen storage, it is important to consider these different factors in defining the ultimate quality of the meat. All the previously mentioned quality parameters can be influenced by any small difference from the rearing of the animal to the slaughter to the processing of the meat all the way up until the point that a consumer tastes the final product. Ultimately, the consumer is the final judge of quality. As food scientists, our goal is to ensure the consumer receives high-quality, wholesome food.

In the following research studies, we attempt to contribute to the knowledge of how freezing impacts the physical quality of meat. First we looked at the effect of freezing rate on the functionality of chicken proteins. Currently drip loss is the primary quality indicator found to be impacted by freezing rate. We expect that other functional quality attributes may also be impacted. Second we looked at the effect of elevated frozen storage temperatures on the oxidation of myoglobin in a model system and on the rate of quality change in whole chicken products. The model system was used to explore the reverse stability of myoglobin oxidation in a system closer to true meat than has been used previously. The exploration in whole chicken products aimed to demonstrate

40 potential opportunities to increase frozen storage temperatures to save energy without compromising quality. Finally the impact of freeze/thaw cycling was explored using magnetic resonance imaging techniques. This was performed to better understand how water mobility and localization impact meat quality.

2.6 REFERENCES

Allen, K. E., & Cornforth, D. P. (2006). Myoglobin oxidation in a model system as affected by nonheme iron and iron chelating agents. Journal of Agricultural and Food Chemistry, 54(26), 10134–10140.

Allied Market Research. (2015). Frozen Food Market is Expected to Reach $306 Billion, by 2020. Retrieved October 8, 2015, from http://www.prnewswire.com/news- releases/frozen-food-market-is-expected-to-reach-306-billion-by-2020---allied- market-research-531215901.html

American Meat Science Association. (2012). AMSA Meat Color Measurement Guidelines. Champaign, IL.

Añón, M. C., & Calvelo, A. (1980). Freezing rate effects on the drip loss of frozen beef. Meat Science, 4(1), 1–14.

Apple, J. K., & Yancey, J. W. S. (2013). Water-Holding Capacity of Meat. In C. Kerth (Ed.), The Science of Meat Quality (pp. 119–145). Oxford, UK: John Wiley & Sons, Inc.

Bailey, A. J. (1972). The basis of meat texture. Journal of the Science of Food and Agriculture, 23, 995–1007.

Barbut, S. (1996). Estimates and detection of the PSE problem in young turkey breast meat. Canadian Journal of Animal Science, 76(3), 455–457.

Bekhit, A., & Faustman, C. (2005). Metmyoglobin reducing activity. Meat Science, 71, 407–439.

Bertram, H. C., & Andersen, H. J. (2004). Applications of NMR in Meat Science. Annual Reports on NMR Spectroscopy, 53(04), 157–202.

Bertram, H. C., Karlsson, a H., Rasmussen, M., Pedersen, O. D., Dønstrup, S., & Andersen, H. J. (2001). Origin of multiexponential T2 relaxation in muscle myowater. Journal of Agricultural and Food Chemistry, 49(6), 3092–3100.

Bertram, H. C., Purslow, P. P., & Andersen, H. J. (2002). Relationship between meat structure, water mobility, and distribution: a low-field nuclear magnetic resonance study. Journal of Agricultural and Food Chemistry, 50(4), 824–9. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11829651

Bertram, H. C., Whittaker, A. K., Andersen, H. J., & Karlsson, A. H. (2004). Visualization of drip channels in meat using NMR microimaging. Meat Science, 68(4), 667–670.

Bertram, H. C., Whittaker, A. K., Shorthose, W. R., Andersen, H. J., & Karlsson, A. H. (2004). Water characteristics in cooked beef as influenced by ageing and high- pressure treatment - An NMR micro imaging study. Meat Science, 66(2), 301–306.

Bevilacqua, A. E., & Zaritzky, N. E. (1980). Ice morphology in frozen beef. Journal of Food Technology, 15(6), 589–597.

Bevilacqua, A. E., & Zaritzky, N. E. (1982). Ice Recrystallization in Frozen Beef. Journal of Food Science1, 47(2), 1410–1414.

Bevilacqua, A., Zaritzky, N., & Calvelo, A. (1979). Histological measurements of ice in frozen beef. Journal of Food Technology, 14, 237–251.

Bonny, J. M., & Renou, J. P. (2002). Water diffusion features as indicators of muscle structure ex vivo. Magnetic Resonance Imaging, 20(5), 395–400.

Bouton, P. E., Harris, P. V, & Shorthose, W. R. (1971). Effect of ultimate pH upon the water-holding capacity and tenderness of mutton. Journal of Food Science, 36(3), 435–439.

Brown, W. D., & Dolev, A. (1963). Effect of Freezing on Autoxidation of Oxymyoglobin Solutions. Journal of Food Science, 28(2), 211–213.

Buttkus, H. (1967). The Reaction of Myosin with Malonaldehyde. Journal of Food Science, 32, 8–10.

Buttkus, H. (1970). Accelerated denaturation of myosin frozen solution . J Food Sci, 35(5), 558–562.

Calkins, C. R., & Hodgen, J. M. (2007). A fresh look at meat flavor. Meat Science, 77(1 SPEC. ISS.), 63–80.

Calvelo, R. (1981). Recent studies on meat freezing. In R. Lawrie (Ed.), Developments in Meat Science - 2 (pp. 125–158). London: Elsevier Applied Science Publishers.

Carpenter, C. E., Cornforth, D. P., & Whittier, D. (2001). Consumer preferences for beef color and packaging did not affect eating satisfaction. Meat Science, 57(4), 359–363.

Chattong, U., Apichartsrangkoon, A., & Bell, A. E. (2007). Effects of hydrocolloid addition and high pressure processing on the rheological properties and microstructure of a commercial ostrich meat product “Yor” (Thai sausage). Meat Science, 76(3), 548–554. 43

Chen, L., & Opara, U. L. (2013). Texture measurement approaches in fresh and processed foods - A review. Food Research International, 51(2), 823–835.

Crigler, J. C., & Dawson, L. E. (1968). Cell Disruption in Broiler Breast Muscle Related to Freezing Time. Journal of Food Science1, 33, 248–250.

Damez, J. L., & Clerjon, S. (2013). Quantifying and predicting meat and meat products quality attributes using electromagnetic waves: An overview. Meat Science, 95(4), 879–896.

Daniel, C. R., Cross, A. J., Koebnick, C., & Sinha, R. (2011). Trends in meat consumption in the USA. Public Health Nutrition, 14(4), 575–83.

Daubert, C., & Foegeding, E. (2010). Rheological Principles for Food Analysis. In S. Nielson (Ed.), Food Analysis2 (4th ed., pp. 541–554). New York, NY: Springer Science & Business Media.

Davey, C. L., & Gilbert, K. V. (1969). Studies in Meat Tenderness . 7 . Changes in the Fine Structure of Meat During Aging. Journal of Food Science, 34(150 ml), 69–74.

DeFreitas, Z., Sebranek, J. G., Olson, D. G., & Carr, J. M. (1997). Carrageenan Effects on Salt-Soluble Meat Proteins in Model Systems. Journal of Food Science, 62(3), 539–543. den Hertog-Meischke, M. J., van Laack, R. J., & Smulders, F. J. (1997). The water- holding capacity of fresh meat. The Veterinary Quarterly, 19(4), 175–181.

Di Luca, A., Mullen, A. M., Elia, G., Davey, G., & Hamill, R. M. (2011). Centrifugal drip is an accessible source for protein indicators of pork ageing and water-holding capacity. Meat Science, 88(2), 261–70.

Dodge, J. W., & Stadelman, W. J. (1959). Studies on Tenderness Evaluation. Poultry Science, 39(1), 184–187.

Doerscher, D. R., Briggs, J. L., & Lonergan, S. M. (2004). Effects of pork collagen on thermal and viscoelastic properties of purified porcine myofibrillar protein gels. Meat Science, 66(1), 181–188.

Empey, W. (1933). Studies on the Refrigeration of Meat. Conditions Determining the Amount of “Drip” from Frozen and Thawed Muscle. Journal of the Society of Chemical Industry, 52(30), 230T–236T.

Fennema, O. (1973). Nature of the Freezing Process. In O. R. Fennema, W. D. Powrie, & E. H. Marth (Eds.), Low-Temperature Preservation of Foods and Living Matter (pp. 150–239). New York: Marcel Dekker, Inc.

Fessas, D., & Schiraldi, A. (2001). Water properties in wheat flour dough I: classical thermogravimetry approach. Food Chemistry, 72(2), 237–244.

Findlay, C. J., Parkin, K. L., & Stanley, D. W. (1986). Differential schanning calorimetry can determine kinetics of thermal denaturation of beef muscle protein. Journal of Food Biochemistry, 10(1), 1–15.

Foucat, L., Taylor, R., Labas, R., Renou, J., & Services, I. (2001). Characterization of frozen fish by NMR imaging and histology. American Laboratory, 33(16), 38–43.

Frozen Food Handling and Merchandising Alliance. (2009). Frozen Food: Handling and Merchandising. McLean, Virginia. Retrieved from http://www.affi.org/assets/resources/public/frozen-food-handling-and- merchandising-2009-f.pdf

Funami, T., Yada, H., & Nakao, Y. (1998). Thermal and rheological properties of curdlan gel in minced pork gel. Food Hydrocolloids, 12, 55–64.

Gotoh, T., & Shikama, K. (1974). Autoxidation of Native Oxymyoglobin from Bovine Heart Muscle. Archives of Biochemistry and Biophysics, 163, 476–481.

Gray, J. I., Pearson, A. M., & Monahan, F. J. (1994). Flavor and aroma problems and their measurement in meat, poultry and fish products. In A. M. Pearson & T. R. Dutson (Eds.), Quality Attributes and their Measurement in Meat, Poultry and Fish Products (Vol. 3, pp. 250–288). Boston, MA: Springer US.

Guiheneuf, T. M., Parker, A. D., Tessier, J. J., & Hall, L. D. (1997). Authentication of the effect of freezing/thawing of pork by quantitative magnetic resonance imaging. Magnetic Resonance in Chemistry, 35(13), S112–S118.

Hamm, R. (1960). Biochemistry of Meat Hydration. Advances in Food Research, 10, 355–463.

Hay, J., Currie, R., Wolfe, F., & Sanders, E. (1972). Effect of Postmortem Aging on Chicken Muscle Fibrils. Journal of Food Science, 38, 981–986.

Honikel, K. O. (1998). Reference methods for the assessment of physical characteristics of meat. Meat Science, 49, 447–457.

Huff-Lonergan, E., & Lonergan, S. M. (2005). Mechanisms of water-holding capacity of meat: The role of postmortem biochemical and structural changes. Meat Science, 71(1), 194–204.

Ingram, M., & Dainty, R. H. (1971). Changes caused by microbes in spoilage of meats. The Journal of Applied Bacteriology, 34(1), 21–39.

Ishioroshi, M., Samejima, K., & Yasui, T. (1979). Heat-induced gelation of myosin: factors of pH and salt concentrations. Journal of Food Science, 44(5), 1280–1284.

James, S. J., & James, C. (2002). Microbiology of Refrigerated Meat. In Meat Refrigeration (pp. 3–20). Boca Raton, FL: CRC Press.

James, S., Nair, C., & Bailey, C. (1984). The effect of the rate of freezing and thawing on the drip loss from frozen beef. In P. Zeuthen, J. Cheftel, C. Eriksson, M. Jul, H. Leniger, P. Linko, … G. Vos (Eds.), Thermal Processing and Quality of Foods (pp. 807–812). Barking, Essex: Elsevier Applied Science Publishers.

Joo, S. ., Kauffman, R. ., Kim, B. ., & Park, G. . (1999). The relationship of sarcoplasmic and myofibrillar protein solubility to colour and water-holding capacity in porcine longissimus muscle. Meat Science, 52(3), 291–297.

Jul, M. (1982). The intracacies of the freezer chain. International Journal of Refrigeration, 5(4), 226–230.

Jul, M. (1984). The Quality of Frozen Food. London: Academic Press, Inc.

Kerr, W. L. (2008). Magnetic Resonance Studies of Food Freezing. In G. A. Webb (Ed.), Modern Magnetic Resonance (pp. 1869–1878). Springer.

Kijowski, J. M., & Mast, M. G. (1988). Effect of Sodium Chloride and Phosphates on the Thermal Properties of Chicken Meat Proteins. Journal of Food Science, 53(2), 367– 370.

Lai, D.-J. (1984). Quantification of the Freeze-Concentration Effect on the Quality Degradation Rates During Frozen Food Storage. Michigan State University.

Lawrie, R., & Ledward, D. (Eds.). (2006a). The Eating Quality of Meat. In Lawrie’s meat science (7th ed., pp. 279–341). Boca Raton, FL: CRC Press.

Lawrie, R., & Ledward, D. A. (2006b). Introduction. In R. Lawrie & D. A. Ledward (Eds.), Lawrie’s meat science (7th ed., pp. 1–14). Boca Raton, FL: CRC Press.

Lee, Y. S., Owens, C. M., & Meullenet, J. F. (2008). The Meullenet-Owens Razor Shear (MORS) for redicting poultry meat tenderness: Its applications and optimization. Journal of Texture Studies, 39(6), 655–672.

LeMeste, M., Champion, D., Roudaut, G., Blond, G., & Simatos, D. (2002). Glass Transition and Food Technology: A Critical Appraisal. Journal of Food Science, 46

67(7), 2444–2458.

Leygonie, C., Britz, T. J., & Hoffman, L. C. (2012). Impact of freezing and thawing on the quality of meat: review. Meat Science, 91(2), 93–8.

Li, K., Heaton, E., & Marion, J. (1969). Freezing Chicken Thighs by Liquid Nitrogen and Sharp Freezing Process. Food Technology, 23(2), 107–109.

Lindeløv, F. (1978). Reactions in frozen foods; the reactions of myosin and single amino acids with some aldehydes. International Journal of Refrigeration.

Listrat, A., Lebret, B., Louveau, I., Astruc, T., Bonnet, M., Lefaucheur, L., … Bugeon, J. (2016). How muscle structure and composition influence meat and flesh quality. Scientific World Journal, 2016.

Lodi, A., Tiziani, S., & Vodovotz, Y. (2007). Molecular changes in soy and wheat breads during storage as probed by nuclear magnetic resonance (NMR). Journal of Agricultural and Food Chemistry, 55(14), 5850–7.

Love, R. (1957). The Expressible Fluid of Fish Fillets. V. - Cell Damage in Fillets Frozen from One Side: the General Picture. Journal of the Science of Food and Agriculture, 8, 238–242.

Macfarlane, J. J., Schmidt, G. R., & Turner, R. H. (1977). Binding of meat pieces: a comparison of myosin, actomyosin and sarcoplasmic proteins as binding agents. Journal Of Food Science, 42(6), 1603–1605.

Mancini, R. A., & Hunt, M. C. (2005). Current research in meat color. Meat Science, 71(1), 100–121.

Miller, A. J., Ackerman, S. A., & Palumbo, S. A. (1980). Effects of Frozen Storage on Functionality of Meat for Processing. Journal of Food Science, 45, 1466–1471.

Moran, T. (1932). Rapid Freezing. Critical Rate of Cooling. Journal of the Society of Chemical Industry, 51, 16T–20T.

National Frozen & Refrigerated Foods Association. (2009). History of Frozen Foods is Long and Varied. Retrieved from https://www.nfraweb.org/resources/articles/details.aspx?ArticleId=18

Nowak, D. (2011). Enzymes in Tenderization of Meat - The System of Calpains and Other Systems - a Review. Pol. J. Food Nutr. Sei, 61(4), 251–256.

Olson, D., Parrish, F., & Stromer, M. (1976). Myofibril Fragmentation and Shear Resistance of Three Bovine Muscles During Postmortem Storage. Journal of Food Science, 41(5), 1036–1041. 47

OpenStax. (2014). Chapter 1. Introduction. Retrieved October 17, 2016, from http://cnx.org/contents/[email protected]:zMTtFGyH@4/Introduction

Penny, I. F. (1975). Use of a centrifuging method to measure the drip of pork Longissimus dorsi slices before and after freezing and thawing. Journal Of The Science Of Food And Agriculture, 26, 1593–1602.

Pérez-Chabela, M. L., Mateo-Oyagüe, J. (2004). Frozen meat: quality and shelf life. In Y. H. Hui (Ed.), Handbook of Food Science, Technology, and Engineering, Volume 3 (pp. 201–214). Boca Raton, FL: CRC/Taylor & Francis.

Petracci, M., Bianchi, M., Mudalal, S., & Cavani, C. (2013). Functional ingredients for poultry meat products. Trends in Food Science and Technology, 33(1), 27–39.

Petrović, L., Grujić, R., & Petrović, M. (1993). Definition of the optimal freezing rate-2. Investigation of the physico-chemical properties of beef M. longissimus dorsi frozen at different freezing rates. Meat Science, 33(3), 319–31.

Petzold, G., & Aguilera, J. M. (2009). Ice Morphology: Fundamentals and Technological Applications in Foods. Food Biophysics, 4(4), 378–396.

Pham, Q. T., & Mawson, R. F. (1997). Moisture migration and ice recrystallization in frozen foods. Quality in Frozen Food, 67–91.

Privalov, P. L., & Makhatadze, G. I. (1993). Contribution of hydration to protein folding thermodynamics. II. The entropy and Gibbs energy of hydration. Journal of Molecular Biology.

Puolanne, E., & Halonen, M. (2010). Theoretical aspects of water-holding in meat. Meat Science, 86(1), 151–165.

Ramsbottom, J. M., & Koonz, C. H. (1939). Freezing temperature as related to drip of frozen-defrosted beef. Food Research, 5, 425–431.

Rasmussen, A., & Andersson, M. (1996). New method for determination of drip loss in pork muscles. In Hildrum & Risvik (Eds.), Proceedings of the 42nd International Congress of Meat Science and Technology (pp. 286–287). Lillehammer, Norway.

Reid, D. S. (1997). Overview of Physical/Chemical Aspects of Freezing. In M. C. Erickson & Y.-C. Hung (Eds.), Quality in Frozen Food (pp. 10–28). New York: Chapman and Hall.

Renou, J. P., Foucat, L., & Bonny, J. M. (2003). Magnetic resonance imaging studies of water interactions in meat. Food Chemistry, 82, 35–39.

ReportLink. (2013). Frozen Food Industry: Market Research Reports, Statistics and 48

Analysis. Retrieved from http://www.reportlinker.com/ci02043/Frozen-Food.html

Reuhs, B. L., & Simsek, S. (2010). Nuclear Magnetic Resonance. In S. S. Nielson (Ed.), Food Analysis (4th ed., pp. 443–456). New York: Springer.

Rosenberg, I. (2005). Electrophoretic Techniques. In Protein Analysis and Purification: Benchtop Techniques (pp. 63–117). Boston, MA: Birkh{ä}user Boston.

Sacks, B., Casey, N. H., Boshof, E., & Zyl, H. Van. (1993). Influence of Freezing Method on Thaw Drip and Protein Loss of Low-Voltage Electrically Stimulated and Non-Stimulated Sheeps ’ Muscle. Meat Science, 34, 235–243.

Samejima, K., Ishioroshi, M., & Yasui, T. (1981). Relative roles of head and tail portions of the molecule in heat-induced gelation of myosin. Journal Of Food Science, 46(5), 1412–1418.

Sano, T., Noguchi, S., & Tsuchiya, T. (1988). Dynamic viscoelastic behavior of natural actomyosin and myosin during thermal gelation. Journal of Food Scinece, 53, 924.

Sayre, R. N. (1970). Chicken Myofibril Fragmentation in Relation to Factors Influencing Tenderness. Journal of Food Science, 35, 7–10.

Scheffler, T.L., J.M. Scheffler, S.C. Kasten, A.A. Sosnicki and D.E. Gerrard. 2013. High glycolytic potential does not predict low ultimate pH in pork. Meat Science, 95: 85- 91.

Shackelford, S. D., Wheeler, T. L., & Koohmaraie, M. (1995). Relationship between shear force and trained sensory panel tenderness ratings of 10 major muscles from Bos indicus and Bos taurus cattle. Journal of Animal Science, 73(11), 3333–3340.

Shahidi, F., Rubin, L. J., & Wood, D. F. (1987). Control of lipid oxidation in cooked meats by combinations of antioxidants and chelators. Food Chemistry, 23(2), 151– 157.

Shen, Q., & Du, M. (2016). Conversion of Muscle to Meat. In W. Przybylski & D. Hopkins (Eds.), Meat Quality: Genetic and Environmental Factors (pp. 81–100). Boca Raton, FL: CRC Press.

Simmons, A. L., Smith, K. B., & Vodovotz, Y. (2012). Soy ingredients stabilize bread dough during frozen storage. Journal of Cereal Science, 56(2), 232–238.

Slade, L., & Levine, H. (1988). Non-Equilibrium Behavior of Small Carbohydrate-Water Systems. Pure and Applied Chemistry, 60(12), 1841–1864.

Slade, L., & Levine, H. (1991). Beyond water activity: recent advances based on an alternative approach to the assessment of food quality and safety. Critical Reviews 49

in Food Science and Nutrition, 30(2-3), 115–360. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1854434

Smith, D. P., Lyon, C.E., Fletcher, D. L. (1988). Comparison of the Allo-Kramer shear and Texture Profile Method of Broiler Breast meat texture Analysis. Poult. Sci.1, 67(1981), 1549–1556.

Smith, G., Blek, K., Sofos, J., Tatum, J., & Williams, S. (2000). Economic Implications of Improved Color Stability in Beef. In E. Decker, C. Faustman, & C. Lopez-Bote (Eds.), Antioxidants in Muscle Foods: Nutritional Strategies to Improve Quality (pp. 397–426). New York: Wiley-Interscience.

Sofos, J. N. (2008). Challenges to meat safety in the 21st century. Meat Science, 78(1-2), 3–13.

Stanley, D. W. (1983). Relation of Structure to Physical Properties of Animal Material. In M. Peleg & E. Bagley (Eds.), Physical Properties of Foods (pp. 157–206). Westport, CT: AVI Publishing Company, Inc.

Szczesniak, A. S. (1998). Effect of Storage on Texture. In I. Taub & R. P. Singh (Eds.), Food Storage Stability (pp. 191–243). Boca Raton, FL: CRC Press.

Szczesniak, A. S. (2002). Texture is a sensory property. Food Quality and Preference, 13(4), 215–225.

Tamilmani, P., & Pandey, M. C. (2016). Thermal analysis of meat and meat products. Journal of Thermal Analysis and Calorimetry, 123(3), 1899–1917.

Tang, J., Faustman, C., & Hoagland, T. A. (2004). Krzywicki Revisited: Equations for Spectrophotometric Determination of Myoglobin Redox Forms in Aqueous Meat Extracts. Journal of Food Science, 69(9), 717–720.

Thomas, L., & Schmidt, S. (2010). Thermal Analysis. In S. Nielson (Ed.), Food Analysis (4th ed., pp. 555–571). New York, NY: Springer Science & Business Media.

Tornberg, E. (2005). Effects of heat on meat proteins - Implications on structure and quality of meat products. Meat Science, 70(3 SPEC. ISS.), 493–508.

Tornberg, E., Andersson, K., & Josell, Å. (1997). The rheological properties of whole and minced meat during cooking as related to sensory and structural characteristics. In Proceedings of the 1st international symposium on food rheology and structure (pp. 16–20/3). Zurich.

Trout, G. R. (1988). Techniques for measuring water-binding capacity in muscle foods-A review of methodology. Meat Science, 23(4), 235–252.

Troy, D. J., & Kerry, J. P. (2010). Consumer perception and the role of science in the meat industry. Meat Science, 86(1), 214–226.

Updike, M. S., Zerby, H. N., Sawdy, J. C., Lilburn, M. S., Kaletunc, G., & Wick, M. P. (2005). Turkey breast meat functionality differences among turkeys selected for body weight and/or breast yield. Meat Science, 71(4), 706–712.

Updike, M. S., Zerby, H., Utrata, K. L., Lilburn, M., Kaletunc, G., & Wick, M. (2006). Proteins associated with thermally induced gelation of turkey breast meat. Journal of Food Science, 71(9).

Veiseth, E., Shackelford, S. D., Wheeler, T. L., & Koohmaraie, M. (2001). Technical Note: Comparison of myofibril fragmentation index from fresh and frozen pork and lamb longissimus. Journal of Animal Science, 79(4), 904–906. http://doi.org/79:904–906

Venturi, L. (2007). NMR Study of Meat as Related to its Structural Organisation. Universita’ di Bologna.

Venturi, L., Rocculi, P., Cavani, C., Placucci, G., Dalla Rosa, M., & Cremonini, M. a. (2007). Water absorption of freeze-dried meat at different water activities: A multianalytical approach using sorption isotherm, differential scanning calorimetry, and nuclear magnetic resonance. Journal of Agricultural and Food Chemistry, 55(26), 10572–10578.

Vodovotz, Y., Vittadini, E., & Sachleben, J. R. (2002). Use of 1H cross-relaxation nuclear magnetic resonance spectroscopy to probe the changes in bread and its components during aging. Carbohydrate Research, 337(2), 147–153.

Wagner, J. R., & Añon, M. C. (2007). Effect of freezing rate on the denaturation of myofibrillar proteins. International Journal of Food Science & Technology, 20(6), 735–744.

Westphalen, A. D., Briggs, J. L., & Lonergan, S. M. (2005). Influence of pH on rheological properties of porcine myofibrillar protein during heat induced gelation. Meat Science, 70(2), 293–299.

Wu, J. Y., Bryant, R. G., & Eads, T. M. (1992). Detection of solid-like components in starch using cross-relaxation and Fourier transform wide-line proton NMR method. Journal of Agricultural and Food Chemistry, 40(3), 449–455.

Xiong, Y. L. (1997). Protein denaturation and functionality losses. Quality in Frozen Food, 111–139.

Yu, X. L., Li, X. B., Zhao, L., Xu, X. L., Ma, H. J., Zhou, G. H., & Boles, J. A. (2010). Effects of Different Freezing Rates and Thawing Rates on the Manufacturing 51

Properties and Structure of Pork. Journal of Muscle Foods, 21(2010), 177–196.

Zachariah, N. Y., & Satterlee, L. D. (1973). Effect of light, pH, and buffer strength on the autoxidation of porcine, ovine and bovine myoglobins at freezing temperatures. Journal of Food Science, 38, 418–420.

Zanoni, S., & Zavanella, L. (2012). Chilled or frozen? Decision strategies for sustainable food supply chains. International Journal of Production Economics, 140(2), 731– 736.

Zaritzky, N. E., & Martino, M. N. (1988). Ice Crystal Size Modifications during Frozen Beef Storage. Journal of Food Science, 53(6), 1631–1637.

CHAPTER 3: Impact of Characteristic Freeze Time on Functionality of Chicken

Breast (Pectoralis major) Meat

ABSTRACT

The time required to freeze various foods has been shown to impact the quality of

these products. Chicken meat is composed of a highly organized structure of

myofibrillar proteins that may be disrupted during freezing causing lowered protein

functionality. This study was performed to assess how characteristic freeze time (CFT)

of chicken breast meat impacts protein functionality. CFT ranged from 2.4 to 102 min.

The functionality attributes measured included moisture content, protein extractability,

brine uptake, and myofibrillar fragmentation index (MFI). Differential Scanning

Calorimetry (DSC) was used to assess the enthalpy of isolated myofibrillar proteins and

rheology was used to assess the viscoelastic properties of salt soluble proteins when

heated. The total moisture and brine uptake were not impacted by CFT. MFI varied but

did not produce a reliable trend. The total enthalpy and relative contribution of

sarcoplasmic proteins to the total enthalpy was significantly higher after freezing,

regardless of rate. The final gel strength, as measured by storage modulus (G’) was

significantly lower in chicken frozen at a medium or slow rate (CFT of 13 and 102 min,

respectively). The gelation behavior also varied during a temperature ramp. These

results suggest that CFT primarily impacts the structure of the myosin head groups,

which would result in the deviations in gelation behavior but not significantly impact

the water binding ability.

3.1 INTRODUCTION

When freezing was first introduced as a food preservation technology, the importance of the freeze time, or, how fast ice forms, was thought to be the primary factor affecting frozen food quality due to the size of the ice crystals formed under various rates (Reid,

1997). The focus of industry practice has been on freezing foods as rapidly as possible to ensure the formation of small ice crystals within the product structure. These steps are thought to ensure the reduction of cellular damage and less impact on the texture of product after freezing. It has been demonstrated that a product which is frozen and immediately thawed can be indistinguishable from the fresh product (Jul, 1984).

However, contradictory results are presented in the body of published literature on the subject. For instance, studies on the formation of drip loss have been found by a number of authors to be decreased with decreased freeze time (K. Li, Heaton, & Marion, 1969;

Moran, 1932; Ramsbottom & Koonz, 1939; Sacks, Casey, Boshof, & Zyl, 1993; Yu et al., 2010) while another group of authors have found no correlation (Crigler & Dawson,

1968; Empey, 1933; S. James, Nair, & Bailey, 1984; Love, 1957) and others have found unique, non-linear relationships (Añón & Calvelo, 1980; Petrović, Grujić, & Petrović,

1993). Clearly, the impact of freeze time is not as straightforward as once thought.

In addition to drip loss, there are a number of other quality attributes that are important to poultry processing including water holding capacity, brine uptake, and the ability to form a thermally-induced meat gel (Fletcher, 2002). All of these attributes are

54 influenced by the state of the protein that goes into the processed product. It is widely known that freezing of meat can have negative effects on the quality of a final product made from this previously-frozen meat (Leygonie, Britz, & Hoffman, 2012). The use of advanced techniques such as thermal analysis, rheology, and/or nuclear magnetic resonance (NMR) may be used to get a better understanding of how different processes, such as freezing, might influence these functional properties of meat proteins (Bertram &

Andersen, 2004; Tamilmani & Pandey, 2016).

The objective of this research was to determine the effect of freeze time on the physical quality of chicken breast meat. Chicken samples were frozen at three different freeze times and examined for functionality including brine uptake and rheological properties and nuclear magnetic resonance (NMR) relaxometry of a thermally-induced meat gel from the salt-soluble proteins. The myofibrillar proteins were examined in more detail using differential scanning calorimetry (DSC) and myofibrillar fragmentation index

(MFI).

3.2 MATERIALS and METHODS

3.2.1 Freezing methods.

A range of freeze times was achieved using programmable temperature-controlled baths. Samples (n = 6) of intact chicken Pectoralis major (PM) (4 cm x 4 cm x 1.5 cm) were vacuum packaged and frozen by one of the following methods: submersion in –

35°C, programmed cool from 4°C to –20°C over 1 h, programmed cool from 4°C to –

20°C over 8 h. All freezes were performed in programmable baths (Model AD07R-40-

A11B, Polyscience, Niles, IL). The freeze times were expressed as “characteristic freeze

55 times” (CFT); the time required to decrease the temperature at a defined location in the sample from the initial freezing point (–1.3°C) to the temperature when 80% of the water is frozen (Añón & Calvelo, 1980). This was calculated using the model from Bartlett,

1944 incorporating an unfreezable water fraction of 7.5% (Delgado & Sun, 2002) and found to be –10°C. All samples were transferred to a –20°C still air freezer and held for no more than 1 week followed by thawing in water at 20°C for 30 min. Unfrozen samples were used as controls.

3.2.2 Brine uptake.

The amount of brine uptake was measured using a modification of the method reported by Updike et al. (2005). Briefly, 2.5 g of chicken PM was minced with a razor blade and weighed into 15 ml conical centrifuge tubes in triplicate. The samples were mixed with 7.5 mL of brine (1.4M NaCl, 0.01 M Na tripolyphosphate, pH 7.6) (C. T. Li

& Wick, 2001). The mixture was held for 1 h at 4°C and then centrifuged at 3,500 × g for

30 min at 4°C. The supernatant was transferred to a tared tube and the mass recorded.

The amounts of brine absorbed by the pellet and remaining in the supernatant were calculated.

3.2.3 Rheology.

Rheological analysis was performed with a modified method as reported by Updike et al. (2005). Samples were prepared using the method for brine uptake described above to collect the salt-soluble protein (SSP) fraction. The supernatant was passed through cheesecloth to remove large particles. A 590 µl aliquot of the supernatant was placed onto a Peltier stage on an AR-2000EX rheometer (TA Instruments) with a 40 mm

56 diameter cone probe with 2° angle. A temperature ramp was run at 1 Hz frequency with a constant stress of 0.1768 Pa. The storage (G’) and loss (G’’) moduli were monitored throughout the run.

3.2.4 Nuclear Magnetic Resonance Cross Relaxation.

Samples of the SSP fraction for NMR analysis were aliquoted into 130.5 x 5 mm

NMR tubes (Bruker, Billerica, MA) and heated in a water bath (Polyscience, at a rate of

1°C/min from 40 to 80°C. A cross-relaxation (or Z-spectroscopy) protocol (CR-NMR) was used to probe the rigidity of the proteins in the thermally-induced meat gel. The CR-

NMR experiments were carried out on a Bruker Avance III HD Ultrashield 600 MHz spectrometer with a 5mm Triple-resonance Inverse (TXI) cryoprobe with Z-Gradients.

The pulse sequence was based on the work of Wu, Bryant, & Eads (1992): a 600 ms square saturation pulse was applied followed by a 90° pulse. The frequency was offset between –50 and 50 kHz to obtain the spectra.

3.2.5 Gel electrophoresis.

The SSP fraction used in the rheological and NMR measurements were also analyzed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) with modifications of the method described by Updike et al. (2006). Samples of SSPs were added in equal volumes to sample buffer (8 M urea, 2M thiourea, 60 mM Tris buffer, pH

6.8, containing 2% SDS, 15% glycerol, 350 mM DTT, and0.1% bromophenol blue). In the case of the non-reducing gel, all reagent concentrations were identical with elimination of DTT to maintain disulfide bonds. Approximately 10 μg of protein was loaded onto each lane of a 10% T gel and the proteins resolved at 10 V cm-1 until the dye

57 front reached the bottom of the gel. Gels were stained with Coomassie Brilliant Blue G-

250 overnight and then destained overnight with 10% acetic acid. After staining and destaining gels were scanned on a flatbed scanner and the images analyzed in Phoretix

1DTM (Nonlinear Dynamics, Newcastle upon Tyne, U.K.). The bands were identified and then analyzed as the percent that the staining intensity of each band contributed to the total staining intensity of all the bands.

3.2.6 Moisture Content.

The moisture content of the whole meat was determined by thermogravimetric analysis (TGA) in a Discovery TGA 550 (TA Instruments). Approximately 10-15 mg of chicken meat was sealed aluminum pans. The pans were punched open immediately before the run and a thermal cycle from room temperature (20-25°C) to 200°C at

10°C/min was used to drive off the moisture while the change in mass was recorded. The derivative curves (DTG) were also plotted to identify different populations of water being removed according to Fessas & Schiraldi (2001).

3.2.7 Myofibrillar extract preparation.

For differential scanning calorimetry (DSC) and myofibrillar fragmentation index

(MFI) analysis, the myofibrillar fraction from the frozen and thawed chicken meat samples was extracted. Meat was blended 1:10 with cold (4°C) myofibril extraction buffer composed of 0.02 M KCl, 0.01 M imidazole, 1 mM EGTA, and 4.62 mM sodium azide for 15 s in a blender (Magic Bullet, Homeland Housewares, Los Angeles, CA). The resulting homogenate was centrifuged at 3,500 x g for 15 min at 4°C. The supernatant was decanted and the pellet re-suspended 1:10 in extraction buffer to rinse the extract and

58 centrifuged again followed by re-suspension in 1:5 buffer twice each followed by centrifugation as described above.

3.2.8 Differential Scanning Calorimetry.

Samples (10-15 mg) of myofibrillar extract were placed in hermetically-sealed aluminum Tzero DSC pans and placed in a Discovery DSC 2500 (TA Instruments). A thermal cycle was performed by equilibrating at 20°C then ramping at 5°C/min to 100°C.

The heat flow of the sample was monitored throughout. The enthalpy of protein denaturation (approximately 55-75°C) was calculated from the resulting thermogram to determine the degree of protein denaturation.

3.2.9 Myofibrillar fragmentation index.

The degree of fragmentation of myofibrils was determined using a modification on the myofibrillar fragmentation index (MFI) outlined by Olson et al. (1976). Briefly, 100 mg of myofibrillar extract was suspended in 900 µl of extraction buffer and vortexed.

The suspension was diluted to a concentration of approximately 0.5 mg/ml. A 300 µl aliquot of the appropriate dilution was added to a 96-well microplate and the absorbance at 540 nm was read on a Biotek Epoch plate reader. The concentration of protein in the diluted sample was determined using the Lowry method based on a standard curve created using BSA. Briefly, 80 µl of sample (or standard) was added to a 1.5 ml

Eppendorf tube with 880 µl of Biuret reagent (Sigma Aldrich, St. Louis, MO), vortexed, and incubated at room temperature for 10 min after which 40 µl of Folin-Ciocalteau’s phenol reagent (Sigma Aldrich) was added. The samples were vortexed and allowed to incubate an additional 30 min at room temperature. Two hundred microliters of each

59 sample was added to a 96-well plate and the absorbance was measured at 750 nm using the plate reader. The MFI is calculated as A540/ [Protein (mg/ml)] * 100.

3.2.10 Statistical analysis.

Differences in the means of the various measured quality attributes was determined by a one-way ANOVA run in JMP 12.0 statistical software (SAS Institute Inc., Cary,

NC). Means were considered significantly different at α = 0.05.

3.3 RESULTS and DISCUSSION

3.3.1 Water binding.

The results of the various quality tests performed on chicken breast samples frozen at different rates are summarized in Table 3.1. The fast, medium, and slow freezes were measured to have CFTs corresponding to 2.4, 13, and 102 min, respectively. The total moisture of the intact chicken meat was not altered significantly by CFT. Similarly, there was no significant difference in the ability of the chicken meat to absorb brine. These results indicate that the CFT did not cause significant changes in the ability of the meat proteins to hold water, either in the native state or in the presence of brine. A previous study in pork found that water holding capacity, as measured by a press method, was not affected by freezing or thawing rate (Yu et al., 2010). As mentioned earlier, a number of studies have shown that drip loss (another indicator of water binding) can be modified by the freeze time (K. Li et al., 1969; Moran, 1932; Ramsbottom & Koonz, 1939; Sacks et al., 1993; Yu et al., 2010). Drip loss was not measured in this study due to the small sample size which would have made it difficult to get an accurate and reproducible measure of drip loss.

It is also important to consider the limitations to freeze time in meat systems since, in many cases, fast freeze times are not feasible due to product dimensions (Xiong, 1997).

However, fast freeze times may be feasible in some processed meat products such as chicken nuggets or tenders, which have significantly smaller dimensions.

3.3.2 Gel-formation.

The resulting SSP fraction generated during the brine uptake testing was used to determine the viscoelastic properties of a thermally-induced meat gel formed by this fraction. The final value of the storage modulus (G’) is thought to correspond with a stronger gel that would be less likely to crack (a significant quality defect in processed meat products) (Updike et al., 2005). The final G’ of the control and the fast frozen samples were significantly higher than the medium and slow frozen samples, indicating there was an impact of CFT on the gel forming ability of the SSPs. There was also a significant difference in the behavior of the storage modulus through the heating cycle as shown in Figure 3.1. It is likely that the differences in the observed rheological behavior are due to modifications of the structure of the myosin head group (S-1 subunit), which contributes to gelation by disulfide bridges (Samejima, Ishioroshi, & Yasui, 1981). The observed differences in gelation behavior through the temperature ramp are likely due to changes in the interactions between head groups as a function of freezing. One hypothesis is that freezing causes cross-linking between the head groups (Figure 3.2) causing an initial appearance of an increased G’, as illustrated by the elevated initial G’ for the frozen samples. This may be due to the freeze-concentration of solutes in the proximity of the myosin molecules (Buttkus, 1970). The differences in gelation behavior between

61 unfrozen and frozen and thawed chicken meat is presented in Figure 3.3. The early stage of gelation (40 – 50°C) in frozen and thawed meat appears to be fairly weak given the decrease in G’ at about 50°C. The second phase is the gelation of the myosin rod groups, which are responsible for the majority of the gel strength.

Extractability of myosin/SSPs has been found to impact the gelation ability of meat

(Tornberg, 2005). Based on this observation, the total protein concentration in the SSP fraction was measured to assess if this could account for the differences in the rheological properties of the thermally-induced meat gel in our study. The protein concentration was not determined to be significantly different among the treatments. Additionally, the relative contribution of myosin in the SSP fraction was determined by SDS-PAGE by analyzing the contribution of the myosin band staining intensity to the total staining intensities for all of the proteins in the samples. The results indicate that there is no significant difference in the extractability of myosin as a result of CFT. These findings correspond to a study in beef semitendinosus muscle that found that freezing rate did not significantly affect the functional properties of the proteins including free sulfhydryl groups, protein solubility or hydrophobicity, color, and emulsion activity and stability

(Farouk, Wieliczko, & Merts, 2004). This again supports the proposed mechanism of structural modifications as a function of freezing primarily being confined to the myosin head group. The myofibrillar swelling and extraction of myosin by a salt and phosphate solution occurs due to the electrostatic dissociation of the rod groups (Offer & Trinick,

1983). We might also expect to see decreases in the final G’ for chicken that has been frozen and stored.

Cross-relaxation NMR was used to probe the thermally-induced meat gel formed from the SSP fraction of chicken meat frozen at different rates to determine if this could be a novel method for the assessment of gel strength. In theory, a firmer gel would result in a CR-NMR spectrum with a larger contribution from the broad component, representative of a more solid-like matrix (Wu et al., 1992). This technique has been used in systems such as starch and gluten (Vodovotz, Vittadini, & Sachleben, 2002); the application in a meat protein system would be a novel use of this NMR method. The results however did not show any indication of differences between the treatments examined in this study (Figure 3.4). It is possible that the method needs further refinement for this system. This is evidenced by the off-center peak in the curves. The high level of salt used in the brine also caused issues with broadening of the band when tuning and matching the magnet, which may result in a loss of sensitivity of the signals obtained.

3.3.3 DSC enthalpies.

The total enthalpy of the myofibrillar extract in the temperature range of protein denaturation was found to differ significantly across the treatments. The control had the lowest enthalpy, while the three CFTs had significantly higher total enthalpy. In theory, the control should have the highest enthalpy, corresponding to the highest level of intact

(not denatured) proteins in a dilute system. However, it has been observed that the precipitation of sarcoplasmic proteins onto myofibrillar proteins during freezing is possible (den Hertog-Meischke, van Laack, & Smulders, 1997). We posit that this is the cause of the increase in total enthalpy of the myofibrillar extract. The change in the

63 relative proportions of the two endothermic peaks evident in the thermograms of the isolated myofibrillar proteins (Figure 3.5). It has been shown that the first endothermic peak (54-58°C) corresponds to myosin and the second (65-67°C) corresponds to collagen and sarcoplasmic proteins (Findlay, Parkin, & Stanley, 1986). This result indicates that after freezing, regardless of rate, there is an increase in the contributions of sarcoplasmic proteins, since the preparation of the myofibrillar extract removes what little collagen may be present in the chicken breast. The increase in the total enthalpy after freezing also supports this reasoning; the sarcoplasmic proteins are likely partially denatured causing aggregation, but the aggregate contains an increase in total protein possible for denaturation during the thermal cycle in the DSC. The increased intermolecular interactions caused by aggregation could also be contributing to the observed increase in enthalpy (Arntfield and Murray, 1981).

A defect in some meats is a condition called “pale, soft, and exudative” or PSE. This condition is most often associated with pork, but similar defects have also been observed in poultry meat (Barbut, 1996; Owens, Hirschler, McKee, Martinez-Dawson, & Sams,

2000; Van Laack, Liu, Smith, & Loveday, 2000). This defect is associated with lowered water holding capacity, pale color, and forms softer gels. Based on the observations in the current study, we propose a similar mechanism is at play in previously frozen chicken breast meat. Primarily, we propose that sarcoplasmic proteins are co-precipitated onto the myofibrils. This has been observed in PSE turkey breast and the authors attributed the co- precipitation of phosphorylase as the causative agent of the change in functionality

(Sosnicki, Greaser, Pietrzak, Pospiech, & Sante, 1998). The appearance or increased

64 intensity of a band at approximately 100 kDa corresponds with the molecular weight of

Phosphorylase a (94 kDa) (Weber & Osborn, 1969), leading us to propose that this mechanism is responsible for the lowered gel strength observed in the rheological measurements.

Wagner & Añon (1985) were able to determine that CFT had an effect on the myosin head region in particular, which was confirmed with ATPase activity measurements. It was noted, also, that myosin was denatured regardless of CFT, but differences were greater at slower CFTs. At fast CFTs (less than 5 min CFT) there was a 3.27% reduction in total enthalpy as opposed to 8.17 and 9.07% loss at intermediate time (20-25 min CFT) or slow time (greater than 60 min CFT), respectively. These results are in agreement with our hypothesis on the reduced gel strength resulting from denaturation of the myosin head groups as a result of freezing.

Few studies have presented data that indicate that freezing method or rate of freezing significantly impact the final eating quality of meat, though it may impact other quality attributes important to meat processors, such as drip loss (S. S. James & James, 2012).

The current finding that CFT affects the rigidity of a thermally-induced meat gel contributes to the notion that this process may have significant impact for meat processors.

3.4 CONCLUSION

While the ability of the proteins to hold brine was not significantly affected by CFT, the ability of the proteins to form a gel was. There was up to an almost 40% decrease in the final G’, a measure of final gel strength, between the unfrozen and slow frozen

65 samples (CFT = 102 min). Since the majority of the water holding ability of the myofibril is dictated by the myosin rod groups, the proposed mechanism of the partial denaturation of the myosin head groups is further supported by the lack of difference in brine uptake.

Though the co-precipitation of sarcoplasmic proteins onto the myofibrils appears to occur, as evidenced by the increase in the total enthalpy as well as the relative contribution of the first peak in the DSC thermograms, they do not significantly influence the ability of the myofibrils to hold water. The influence of CFT is likely caused by the formation of different sized ice crystals. Large ice crystals formed due to slow freezing have a higher surface energy which would result in a greater dehydration of the proteins.

This results in more denaturation and consequently decreased gel strength. The change in the shape of the rheograms at the temperature range of 45 to 55°C also supports the hypothesis that freezing is causing modification of the myosin head groups, altering the final gel strength. Though the methods here cannot confirm the actual mechanism causing this difference, it begins to explain how previously frozen meat might result in lower quality processed products.

3.5 TABLES and FIGURES

Protein Enthalpy SSP Myosin Brine MC1 Total Peak 1 Peak 2 Protein Band uptake MFI Freezing (% (% meat Rate (%) (J/g) (%) (%) (mg/ml) intensity) mass) Control 71.12% 1.201c 77.67%a 22.33% 0.68ab 14.43 24.11% 74.72% Fast 71.13% 1.642a 74.58%b 25.42% 0.59c 14.54 22.38% 79.00% Medium 70.09% 1.374b 74.05%b 25.94% 0.73a 13.73 21.77% 80.27% Slow 70.01% 1.528ab 75.35%ab 24.65% 0.61bc 14.50 23.13%b 83.38% 1Total moisture content (w.b.)

Figure 3.1. Representative curves of G' over a temperature sweep from 40 to 80°C of chicken SSPs extracted from chicken breast samples subjected to different freezing rates.

Figure 3.2. Proposed mechanism for modification of myosin as a function of freezing and thawing.

Control

Fast

Medium

Slow

-20 -15 -10 -5 0 5 10 15 20 Offset (kHz)

Figure 3.4. Cross-relaxation spectra for thermally-induced meat gels formed from the SSP fraction of chicken breast meat frozen at different rates.

Temperature (°C) 20 30 40 50 60 70 80 90 100 -0.35

-0.36

-0.37 Control

Fast Relative Relative flow heat (W/g) Medium Slow -0.38

Figure 3.5. Representative DSC thermograms from 20 to 100°C for myofibrillar extract from chicken breast samples subjected to different freezing rates. The peak around 60°C corresponds to myosin while the peak around 70°C corresponds to sarcoplasmic proteins. After freezing the increase in the second peak shows a co-precipitation of sarcoplasmic proteins onto the myofibrillar proteins.

3.6 REFERENCES

Añón, M. C., & Calvelo, A. (1980). Freezing rate effects on the drip loss of frozen beef. Meat Science, 4(1), 1–14.

Arntfield, S. D., & Murray, E. D. (1981). The Influence of Processing Parameters on Food Protein Functionality I. Differential Scanning Calorimetry as an Indicator of Protein Denaturation. Canadian Institute of Food Science and Technology Journal, 14(4), 289–294. http://doi.org/10.1016/S0315-5463(81)72929-8

Barbut, S. (1996). Estimates and detection of the PSE problem in young turkey breast meat. Canadian Journal of Animal Science, 76(3), 455–457.

Bartlett, L. (1944). A thermodynamic examination of the latent heat of food. Refrigeration Engineering, 47, 377.

Buttkus, H. (1970). Accelerated denaturation of myosin frozen solution. J Food Sci, 35(5), 558–562. http://doi.org/10.1111/j.1365-2621.1970.tb04808.x

Bertram, H. C., & Andersen, H. J. (2004). Applications of NMR in Meat Science. Annual Reports on NMR Spectroscopy, 53(04), 157–202.

Crigler, J. C., & Dawson, L. E. (1968). Cell Disruption in Broiler Breast Muscle Related to Freezing Time. Journal of Food Science1, 33, 248–250.

Delgado, A. E., & Sun, D. W. (2002). Desorption isotherms and glass transition temperature for chicken meat. Journal of Food Engineering, 55(1), 1–8. den Hertog-Meischke, M. J., van Laack, R. J., & Smulders, F. J. (1997). The water- holding capacity of fresh meat. The Veterinary Quarterly, 19(4), 175–181.

Farouk, M. M., Wieliczko, K. J., & Merts, I. (2004). Ultra-fast freezing and low storage temperatures are not necessary to maintain the functional properties of manufacturing beef. Meat Science, 66(1), 171–179.

Fessas, D., & Schiraldi, A. (2001). Water properties in wheat flour dough I: classical thermogravimetry approach. Food Chemistry, 72(2), 237–244.

Findlay, C. J., Parkin, K. L., & Stanley, D. W. (1986). Differential scanning calorimetry can determine kinetics of thermal denaturation of beef muscle protein. Journal of Food Biochemistry, 10(1), 1–15.

Fletcher, D. L. (2002). Poultry meat quality. World’s Poultry Science Journal, 58(02), 131–145.

Jul, M. (1984). The Quality of Frozen Food. London: Academic Press, Inc.

Leygonie, C., Britz, T. J., & Hoffman, L. C. (2012). Impact of freezing and thawing on the quality of meat: review. Meat Science, 91(2), 93–8.

Li, C. T., & Wick, M. (2001). Improvement of the physicochemical properties of pale soft and exudative (PSE) pork meat products with an extract from mechanically deboned turkey meat (MDTM). Meat Science, 58(2), 189–195.

Li, K., Heaton, E., & Marion, J. (1969). Freezing Chicken Thighs by Liquid Nitrogen and Sharp Freezing Process. Food Technology, 23(2), 107–109.

Love, R. (1957). The Expressible Fluid of Fish Fillets. V. - Cell Damage in Fillets Frozen from One Side: the General Picture. Journal of the Science of Food and Agriculture, 8, 238–242.

Moran, T. (1932). Rapid Freezing. Critical Rate of Cooling. Journal of the Society of Chemical Industry, 51, 16T–20T.

Offer, G., & Trinick, J. (1983). On the mechanism of water holding in meat: The swelling and shrinking of myofibrils. Meat Science, 8(4), 245–281.

Olson, D., Parrish, F., & Stromer, M. (1976). Myofibril Fragmentation and Shear Resistance of Three Bovine Muscles During Postmortem Storage. Journal of Food Science, 41(5), 1036–1041.

Owens, C. M., Hirschler, E. M., McKee, S., Martinez-Dawson, R., & Sams, A. R. (2000). The characterization and incidence of pale, soft, and exudative broiler meat in a commercial processing plant. Poultry Science, 79(4), 553–558.

Ramsbottom, J. M., & Koonz, C. H. (1939). Freezing temperature as related to drip of frozen-defrosted beef. Food Research, 5, 425–431.

Reid, D. S. (1997). Overview of Physical/Chemical Aspects of Freezing. In M. C. Erickson & Y.-C. Hung (Eds.), Quality in Frozen Food (pp. 10–28). New York: Chapman and Hall.

Samejima, K., Ishioroshi, M., & Yasui, T. (1981). Relative roles of head and tail portions of the molecule in heat-induced gelation of myosin. Journal Of Food Science, 46(5), 1412–1418.

Sosnicki, A. A., Greaser, M. L., Pietrzak, M., Pospiech, E., & Sante, V. (1998). PSE-like Syndrome in Breast Muscle of Domestic Turkeys: A Review. Journal of Muscle Foods, 9(1), 13–23.

Tamilmani, P., & Pandey, M. C. (2016). Thermal analysis of meat and meat products. Journal of Thermal Analysis and Calorimetry, 123(3), 1899–1917.

Tornberg, E. (2005). Effects of heat on meat proteins - Implications on structure and quality of meat products. Meat Science, 70(3 SPEC. ISS.), 493–508.

Updike, M. S., Zerby, H., Utrata, K. L., Lilburn, M., Kaletunc, G., & Wick, M. (2006). Proteins associated with thermally induced gelation of turkey breast meat. Journal of Food Science, 71(9).

Van Laack, R. L., Liu, C. H., Smith, M. O., & Loveday, H. D. (2000). Characteristics of pale, soft, exudative broiler breast meat. Poultry Science, 79, 1057–1061.

Wagner, J. R., & Añon, M. C. (1985). Effect of freezing rate on the denaturation of myofibrillar proteins. Journal of Food Technology, 20(6), 735–744.

Weber, K., & Osborn, M. (1969). The Reliability of Molecular Weight Determinations by Dodecyl The Reliability of Molecular Weight Determinations by Dodecyl Sulfate- Polyacrylamide Gel Electrophoresis. Journal of Biological Chemistry, 244, 4406– 75

4412.

Xiong, Y. L. (1997). Protein denaturation and functionality losses. Quality in Frozen Food, 111–139.

Yu, X. L., Li, X. B., Zhao, L., Xu, X. L., Ma, H. J., Zhou, G. H., & Boles, J. A. (2010). Effects of Different Freezing Rates and Thawing Rates on the Manufacturing Properties and Structure of Pork. Journal of Muscle Foods, 21(2010), 177–196.

CHAPTER 4: Kinetics of physical quality loss in a model meat system and whole

meat products under frozen storage conditions

Abstract

The kinetics of quality loss are an important factor in the prediction of quality loss for foods under various storage conditions. Previous studies indicate that the oxidation of myoglobin under frozen storage conditions exhibits a rate acceleration, termed reverse stability. The goal of the present study was to develop a model for myoglobin oxidation to incorporate the phenomenon of reverse stability in a model system. Additionally, this study examined whole chicken products to determine if this effect was present in an actual food system, as compared to a model system. The model system was an aqueous extract from beef which was stored under a range of temperatures, both unfrozen and frozen. The kinetic analysis showed that under frozen conditions this reaction exhibited a rate acceleration with a local maximum around −20°C. The addition of NaCl to the model system caused higher rates at all temperatures, even above the initial above the initial freezing temperature. This observation suggests that this reaction is dependent on salt concentration as well as temperature. Whole chicken breasts and ground chicken patties were used to explore kinetics of quality degradation at over a range of frozen storage temperatures from −20 to −5°C, for up to 70 days. Within this time frame, storage temperature did not have a significant impact on the rates of quality loss for any of the

77 measured attributes, including moisture content, water holding capacity, drip loss, and cook loss. The freezing rate of patties also did not impact rates of quality loss. Though the model system is not representative of an actual meat system, these studies do show the potential to consider the use of higher frozen storage temperatures without significant impact on physical quality attributes, and gain the advantage of energy conservation.

4.1 Introduction

Freezing is one of the major forms of food preservation allowing the extension of

the shelf-life of many foods with a combination of lowered temperature and lowered

water availability. These two mechanisms work synergistically to reduce the rate of

microbial growth and chemical reactions. However, freezing can also result in structural

damage, protein denaturation, and loss of functionality which, in meat systems, can

result in decreasing downstream processing capabilities, including water binding

functions. The current recommended temperature for frozen storage is −18°C (0°F)

(Frozen Food Handling and Merchandising Alliance, 2009), which was found to keep a

majority of food products within an acceptable quality difference (usually as determined

by a consumer panel) for up to one year. However, this standard has been called into

question in the literature (James & James, 2012); the data used to generate this

recommendation, it has been pointed out, is based on outdated technology and data

(Perez-Chabela et al., 2004) . Similarly, Jul (1982) pointed out that many of the current

practices in the frozen food industry are based on limited studies and should not

necessarily be applied broadly to all foods indiscriminately.

Additionally, a large amount of energy is required to keep the frozen product at

−18°C during the entire storage period (Zanoni & Zavanella, 2012). Anecdotal evidence suggests many frozen foods are not kept for long periods of time (~12 months) and it may be possible to raise commercial frozen storage temperatures without significant loss in quality. However, before these changes can be implemented, it is critical to understand the impact these changes would have on the final product quality. In attempting to model the kinetics of frozen storage, it is important to note that certain reactions have been observed to have an increased rate in the frozen state. A number of these reactions are summarized by Fennema (1973) and include various hydrolysis, insolubilization and oxidation reactions which can cause a deterioration of food quality.

This phenomenon, termed ‘reverse stability,’ may be caused by a number of mechanisms including freeze-concentration effect. This is caused by the concentration of reactants in the unfrozen aqueous phase as ice crystallizes as pure water. The increased concentration of solutes results in a higher statistical probability for a reaction to occur resulting in a higher rate than would normally be anticipated by traditional

Arrhenius kinetics, which dictates that a decrease in temperature results in an exponential decrease in reaction rate. However, rates are related to both temperature and concentration of reactants. Concentrations of reactants could reverse the role of temperature. In addition to the increased probability of reactants interacting, protein- based systems are also susceptible to denaturation by the freeze-concentration of salts.

The high salt concentration causes a disruption of the electrostatic forces responsible for maintaining the native tertiary and quaternary structure (Xiong, 1997). This can lead to denaturation, dissociation of subunits, and aggregation of the protein molecules.

One reaction that follows this trend is the oxidation of oxymyoglobin to metmyoglobin, the reaction that results in the browning of raw and cooked meat. This reaction has been found to have a maximum rate around −15°C (Brown & Dolev,

1963). Other research suggests the rate maximum is closer to temperatures just below the freezing point (−2°C to −3°C) (Lai, 1984). The reaction of malondialdehyde (a marker of oxidative stress) with myosin has been observed to increase significantly upon freezing, indicating denaturation of myosin that is an indication of loss of meat protein functionality (Buttkus, 1967; Lindeløv, 1978). Buttkus (1970) analyzed the denaturation of myosin in a high-salt (0.45 M) solution and found that above freezing increased salt did not result in increased denaturation, but denaturation was maximized at −10°C. The functionality of meat depends on the downstream application but includes various measurable factors including drip loss (the loss of water by fresh meat), water holding capacity (WHC), thermally induced meat gel formation, and texture among others mentioned previously. It is important to understand the possible contributions of reverse stability to the loss of quality in frozen foods, especially when attempting to model quality loss.

This study had two primary aims: 1) to establish a kinetic model for the oxidation of myoglobin in a model system and 2) to explore the effect of select frozen storage conditions on two types of whole meat products. The first aim was accomplished using a model system (an aqueous sarcoplasmic extract from beef which contains all the myoglobin in muscle cells) to establish the kinetic model due to the ease of measurements and consistency of the system. The whole meat products used were

chicken breast and ground chicken patties (made from chicken breast). These were

chosen because of the uniformity of the available product and the relative simplicity of

the muscle, which contains only a single muscle fiber type. Though the model system

for aim 1 was obtained from beef and the whole meat systems for aim 2 were from

chicken, the concepts are expected to be consistent.

4.2 Materials and Methods

4.2.1 Preparation of dehydrated sarcoplasmic extract.

Beef muscle (biceps femoris) was obtained from a local grocer (The Kroger Co.,

Cincinnati, OH) and the sarcoplasmic extract created the same day. The extraction was

performed using a modified method from Huffman et al. (2012). Briefly, 1 part beef

muscle was blended in a food processor with 2 parts ice-cold phosphate buffer (0.02 M

KCl, 0.002 M KH2PO4, pH 6.8) for 30 seconds and centrifuged at 10,000 × g for 10 min

at 4°C. The supernatant, containing sarcoplasmic proteins and other constituents was

removed for analysis and the pellet was discarded. The supernatant was pre-filtered to

remove residual fat and then filtered through a 0.45 μm filter to reduce the potential for

microorganisms to grow in the extract and disrupt measurements. The resulting material

was tested for pH and electrical conductivity (EC). The filtered sarcoplasmic extract

was shell frozen in 20 ml aliquots in 50 ml conical centrifuge tubes and freeze-dried for

24 to 36 h for storage at −20°C.

4.2.2 Kinetic assay.

For each kinetic assay experiment, the appropriate amount of sarcoplasmic extract

was reconstituted with deionized water to the original volume and allowed to rehydrate

81 for 1 h at 4°C. Salt (NaCl; Fisher Scientific, Fairlawn, NJ) was added to subsamples of rehydrated sarcoplasmic extract at 0.5% (w/w). Prepared sarcoplasmic extracts were split into 0.5 ml aliquots in 1.5 ml polypropylene microcentrifuge tubes (Fisher

Scientific). Tubes were stored at various temperatures in a controlled temperature (±

0.1°C), recirculating bath (PolyScience, Niles, IL). For storage temperatures below the freezing point of the solution, tubes were first frozen to −30°C and then allowed to equilibrate to the storage temperature. Temperatures were monitored using a K-type flexible thermocouple and data logger placed into an extra tube alongside the test samples.

Sampling was performed by removing tubes in duplicate from the bath at various time points depending on the temperature. For frozen conditions, tubes were thawed in room temperature water until all ice was visibly melted (less than 5 min). Tubes were vortexed and diluted 1:1 in phosphate buffered saline (PBS; Mediatech, Inc., Manassas,

VA). Diluted samples were then syringe filtered with a 0.45 μm filter (BD, Franklin

Lakes, NJ) to remove any protein precipitates that may have formed during storage which interfere with spectral readings. Preliminary tests showed that filtration did not have a significant effect on oxymyoglobin measurement in fresh samples with no precipitate suggesting the filtration does not interfere with measurements. Dilutions were aliquoted (300 µl) into individual wells on a 96-well plate and wells were read on a plate reader (Epoch, BioTek, Winooski, VT) with a spectral sweep scan from 450 to

700 nm in 1 nm steps. The absorbance at 503, 525, 557 and 582 nm were used to calculate the relative percentages of deoxy-, oxy- and metmyoglobin based on empirical

equations (Tang, Faustman, & Hoagland, 2004). The change in the relative amount of

oxymyoglobin was used to determine the rate of oxidation.

4.2.3 Chicken samples.

Two types of chicken products were examined to explore the effect of storage

temperature on meat quality: whole, intact chicken breasts and ground chicken patties.

The chicken breasts were obtained from a local purveyor for both products (Gerber

Poultry, Kidron, OH). For the intact chicken breasts, individual breasts were placed

directly into Cryovac bags and vacuum packaged prior to freezing. For the ground

chicken patties, breasts were ground first through a 3/8” plate using a Hobart 4732A

grinder (Hobart, Troy, OH). Afterwards, the ground meat was placed into a Holly 100

commercial food mixer (Hollymatic Corp., Countryside, IL) and blended with ice-cold

brine (5.21% NaCl, 3.45% tripolyphosphate, 91.34% water) to a 15% pump and mixed

for 15 min. The brined meat was ground again with an 8 mm plate and formed into

patties (approximately 100 g each) with a Hollymatic Super Model 54 food portioning

machine onto wax-coated patty paper. Patties were placed into individual Cryovac bags

and vacuum sealed prior to freezing.

4.2.4 Freezing and frozen storage.

Chicken products were frozen using a small pilot blast air freezer (Henny Penny,

Inc., Eaton, OH) at −35°C or in a still air, manual defrost freezer (Kenmore 21042,

Sears Brands LLC, Chicago, IL) at −20°C. The whole breasts were frozen in the still air

freezer only and the patties were frozen with both still and blast air to compare how

freezing rate may affect quality during storage. All products were evenly divided and

stored in nine 7.0 cu. ft. chest freezers (Model NS-CZ70WH6, Insignia Systems, Inc.,

Minneapolis, MN) each controlled at one of three temperatures (−5, −10, and either −18

or −20 ± 0.5°C) with external digital temperature controllers (Model A419, Johnson

Controls, Milwaukee, WI). Samples were pulled, thawed overnight at 4°C, and analyzed

every week or every other week for up to 70 days.

4.2.5 Water holding capacity.

WHC of each PM was determined as described by Li and Wick (2001). Briefly,

approximately 500 mg of meat sample is placed between two Whatman® No.1 filter

papers. Filter papers are then placed between two Teflon® plates then placed between

the plates of a Carver Press and subjected to a mechanical force of 500 psi for 1 min.

After compression, the filter papers are separated from the plates, and the images

scanned. The compression produced two circles, an inner circle corresponding to meat

film area and an outer circle corresponding to the total surface area. The % moisture of

each sample was determined by air-drying method (100oC, 18 hours), described by

AOAC (1995). Total moisture (mg) is calculated using the following equation:

Total moisture, mg = (Original meat weight, g) x (moisture %) x 1000 mg/g.

The areas of the two circles are determined by image analysis and the area between

the two traced circles on the filter paper is defined as amount of free water in meat. The

compression procedure is also employed to determine the wet area of 20 mg of water as

a reference for free water (n = 5). The result from each sample is recorded, and WHC

(%) is calculated as follows:

WHC = (free water, mg / original meat weight, mg) x 100.

4.2.6 Drip loss.

Drip loss was measured by mass difference after thawing on the whole breast

samples. The weight before freezing was recorded. The meat was then frozen and

thawed, as described above. After thawing, the meat was removed from the package and

re-weighed. Drip loss was calculated as the percentage of mass lost after thawing.

4.2.7 Cook loss.

The amount of cook loss was determined using a modification of the cook method

outlined by Petracci, Sirri, Mazzoni, & Meluzzi (2013). Briefly, chicken breasts or

patties were thawed as described above and left in their original packages. The packages

were weighed before cooking. The vacuum-sealed packages were submerged in a water

bath (80°C) until the final internal temperature measured 80°C on an extra sample not

used in the further analysis. The chicken breasts or patties were then submerged in an

ice water bath to cool to 4°C within an hour of cooking, according to Appendix B

cooling protocols (USDA, 1999). The samples were opened, and the breasts were

blotted gently with a paper towel then reweighed. The amount of cook loss was

determined as the percentage of weight lost by the sample.

4.2.8 Statistical Analysis.

The kinetic data for oxidation of myoglobin in solution was analyzed using SAS 9.3

(SAS Institute Inc., Cary, NC) to perform Monte Carlo simulations in order to generate

rate constants from the obtained data (Hessler, 1997). The code can be found in

Appendix A: SAS code for Monte Carlo simulation of kinetic data. For the change in quality

in whole chicken meat products, regressions were run using JMP 12 software (SAS

Institute Inc.). Regressions were considered significant at a level of P < 0.05. Rates

were considered significantly different if the 95% confidence intervals of any pair of

slopes did not overlap.

4.3 Results and Discussion

4.3.1 Kinetics of myoglobin oxidation in a model system

The rate of the reaction was found to follow first-order behavior and rate constants

(k) were calculated as the slope of a plot of the concentration of oxymyoglobin over

time. Rate constants were calculated over the temperature range explored; the natural

logarithms of the rate constants were plotted against the inverse of the absolute

temperature to create an Arrhenius plot (Figure 4.1). The oxidation of oxymyoglobin to

metmyoglobin followed traditional Arrhenius kinetics in unfrozen solutions, which is to

say the change in the reaction rate is log-linear with the inverse of absolute temperature.

The addition of 0.5% NaCl was designed to bring the system closer to physiological

ionic strength. This increase osmotic strength resulted in an increased rate of oxidation,

with rate constants at the same temperature higher in the solution with added NaCl

under both unfrozen and frozen conditions. Under frozen conditions, there is an initial

decrease in the rate of oxidation. However, as temperature decreased, the rate of

oxidation increased with a maximum rate around −20°C. This trend was evident in both

salt levels (0 and 0.5%), though the effect of this rate acceleration was greater in the

solution with 0.5% NaCl added.

The effect of elevated NaCl concentration (up to 1.5% added salt) was explored

only under unfrozen conditions due to the formation of a significant precipitate when

86 frozen. The rate of oxidation was found to increase with additional NaCl at any given temperature, as mentioned previously; in addition, the slope of the Arrhenius plot decreased with added. These slopes were used to calculate the activation energy (Ea) at each salt concentration. The Ea for the oxidation of myoglobin decreased linearly with added NaCl, indicating that this reaction becomes less sensitive to temperature with increased salt concentration (Figure 4.2). The calculated Ea values (ranging from 82.6 to 124.1 kJ/mol) are similar to previously reported values of 110.9 kJ/mol (Gotoh &

Shikama, 1974) and 118 kJ/mol (Lai, 1984). The elevated rate with increased dissolved solids is consistent with Lai (1984), however this study found a change in the Ea where

Lai did not. This may be caused by the increased complexity of the system (Lai explored pure myoglobin in buffer) which results in the impact of different solutes and enzymes within the matrix, all of which may impact the rate of oxidation differently. In fact, Lai found that pure myoglobin in deionized water did not exhibit a rate acceleration under frozen conditions, indicating the cause of this phenomenon is not increased proximity of the myoglobin moieties themselves, but an increased localization of other dissolved species within the system. It has been observed previously that increased in the ionic strength of a solution of myoglobin results in decreased stability, particularly in buffers containing NaCl, as we have studied here (Renerre, Anton, &

Gatellier, 1992).

The results of this study agree with those of Brown and Dolev (1963) who found a local minimum for the rate of oxymyoglobin oxidation at −5°C in aqueous solutions from beef and tuna in phosphate buffer. These authors performed studies on the

87 oxidation of pure tuna and beef oxymyoglobin in phosphate buffer that suggest a maximum at −15°C ranging from 3 to 150 times the rate in unfrozen solution at −5°C.

Another study (Zachariah & Satterlee, 1973) suggests a maximum of oxidation around

−12°C was observed. Other research suggests the rate maximum is closer to temperatures just below the freezing point (−2 to −3°C) (Lai, 1984).

The concept of non-Arrhenius behavior in various systems has been explored previously and alternative models have been explored in attempts to more accurately predict this deviant kinetic behavior (Barsa, Normand, & Peleg, 2012; Peleg, Engel,

Gonzalez-Martinez, & Corradini, 2002; Peleg, Normand, & Corradini, 2012; Wang &

Roberts, 2013). However, these models only account for relatively minor differentiations from Arrhenius kinetics and cannot account for the parabolic trend observed in the frozen samples observed here. The model proposed by Lai (1984) for the oxidation of myoglobin under frozen conditions in solution introduces the concept of a concentration factor, which is a modifier for the rate constant in a traditional

Arrhenius equation based on the unfrozen water (UFW) fraction in the system. The amount of UFW decreases exponentially with temperature; this means that the concentration factor has the biggest impact at temperatures just below the freezing point. In the current study, this factor is not able to account for the large deviations at such low temperatures.

In the investigation of the influence of increased ionic strength on the oxidation of myoglobin, NaCl was added to the rehydrated sarcoplasmic extract up to 1.5% (w/w).

When frozen and stored at temperatures between −1 and −10°C, a precipitate was

observed in the solution in as little as 2 days of storage. This precipitate interfered with

the measurement of absorbance in these samples and filtration of the samples resulted in

concentrations of oxymyoglobin inconsistent with the previously observed trends in

oxidation indicating the likelihood that myoglobin was being precipitated during

storage. It has been observed previously that proteins in ox muscle sarcoplasmic extract

are irreversibly denatured when stored under frozen conditions with a maximum rate

between −2 and −3°C (Finn, 1932) which is consistent with the observations made in

this study.

The data obtained at elevated salt at temperatures above freezing were used to

construct a model to account for both temperature and salt concentration. The resulting

model is as follows:

ln(푘) = 0.14685 ∗ 푇 + 0.72279 ∗ %푁푎퐶푙 − 4.35018

This model can be found plotted in Figure 4.3 against the model shown in Figure

4.1. The salt concentration was modelled using the unfrozen water fraction equation of

Heldman (1974) and the composition of the sarcoplasmic extract. It is evident that

simply accounting for the increase in salt concentration in not enough to account for the

large deviations from Arrhenius kinetics at sub-freezing temperatures indicating other

mechanisms must be at play in this system. These might include cold denaturation

(Franks, 1995) or destabilization of the metmyoglobin reducing system (Bekhit &

Faustman, 2005).

4.3.2 Effect of storage temperature on quality of frozen chicken breast meat.

The effect of frozen storage temperature was explored in two different chicken meat

89 systems: whole chicken breast and ground chicken patties. The chicken patties were a ground and formulated product, commonly referred to in the processed meat industry as

‘enhanced.’ Chicken breast meat was chosen as the model system for these experiments due to the minimal bird-to-bird variation within a single flock (reared under the same environmental and feeding conditions) and breast muscle contains little fat and almost exclusively a single muscle fiber type, namely fast-twitch glycolytic type IIB fibers

(Kiessling, 1977). Ideally this should have optimized the opportunities to detect differences in time/temperature treatments by minimizing the external factors affecting meat quality. Different freezing rates were tested in the ground chicken patties to determine if freezing rate would have an influence on the influence of temperature. The freezing rate is expressed as the characteristic freezing time (CFT), which is, as defined by Bevilacqua, Zaritzky, & Calvelo (1979), the time from the initial freezing point

(−0.8°C in the current study) and the temperature where 80% of the water is frozen as ice (−4°C, as determined by the model from Heldman, 1974). The whole breasts were frozen under still air conditions resulting in a CFT or 424 min (7.1 h). The ground chicken patties were found to have a CFT of 33 min (0.5 h) or 376 min (6.3 h), under blast and still air conditions respectively.

Statistical analysis indicated that there was no significant difference between the nine different freezers, indicating that no effect was introduced by the experimental unit of the freezer. In order to compare the rate of quality loss of the various measured parameters, kinetic rate constants were calculated and evaluated to determine how well the data can be used in a predictive capacity. First, the order of the reaction was

90 determined (Table 4.1) by fitting each quality attribute to one of four reaction orders (0,

½, 1st. and 2nd) and the order with the minimal standard of squared errors (SSE) and maximized correlation coefficient (R2) was chosen to calculate the rate constants presented in Table 4.2 for whole chicken breasts and Table 4.3 for ground chicken patties.

The color of the meat was measured in terms of the L*, a*, and b* color parameters.

The color attributes were not found to fit any standard kinetic model in a statistically significant way and as such are not presented in these tables. The L* was not significantly difference across all 3 test systems (whole breast, patty frozen in still air, patty frozen in blast air). The a* and b* values were significantly higher in the patties than in the whole breast, which may be due to the matrix effects of the intact breast muscle as opposed to the homogeneous material of the patties. However, over time there was no significant trend in the changes of any of the color parameters in any of the systems measured.

The amount of drip loss (the mass of water lost after thawing) was only measured in the whole chicken breast samples because the amount was too little in the patties and was absorbed by the patty paper in the package. The patties, being a formulated product, are expected to have less drip loss due to the added salt and phosphate, which increase the water binding of the chicken meat proteins. Calculated k values indicate that at −5°C there was a significant trend, however at −10 and −20°C, the slopes are not significantly different from zero, indicating that at −10°C, we are able to maintain quality (in terms of drip loss) that is not significantly different from lower temperatures.

Additionally, there was no significant difference across the three temperatures explored here.

The total moisture content of the whole breasts was significantly lower than the patties at the beginning of storage, which is logical since the patties were formulated with 15% additional brine, the majority of which is water. Again, there were no significant differences between the three systems tested at any of the temperatures.

The WHC was significantly higher at the beginning of storage in patties, regardless of freezing method. The whole breasts have k values that indicate there is an increase in

WHC, which does not make physical sense and may just be an artifact of the noise within the system or may reflect a physical opening of the meat structure, allowing the ingress of water after thawing. The blast frozen patties do not have significant k values at any temperature, indicating there was no significant change over time regardless of temperature. The still air frozen patties have significant change with time at −5°C and

−10°C, but not at −20°C. Regardless, there was found to be no significant difference in the WHC of patties regardless of freezing rate or storage temperature. These largely inconsistent results may be cause by a number of factors, including biological variation within the samples, variation in samples based on location, or lack of sensitivity of the method.

The amount of cook loss was significantly higher in the whole breasts than in patties frozen by either freezing method. Again, the formulation of the patties including the salt and phosphate is designed to ensure the meat holds water even after cooking. While all the systems exhibited a significant increase with time, there were no significant

92 differences in the rate of increase across all three systems and all three temperatures studied.

Zaritzky & Martino (1988) determined that the limit ice diameter (D1, maximum ice crystal size) could be determined in meat using the muscle fiber dimensions and calculated the value to be about 61 µm in beef. The time to reach D1 was dependent upon temperature and initial ice crystal diameter (Do). Since ice crystal size was not measured in these studies, it is possible that the effect of storage temperature was not found to be significant due to a large Do after the freezing process was completed. This would cause the limit dimeter to be achieved more rapidly and differences would be minimized since the quality attributes measured here are primarily affected by ice recrystallization. Additionally, Bevilacqua & Zaritzky (1980) found that at times longer than 4 or 20 min, intracellular ice was not formed in beef when frozen parallel or perpendicular to the fiber direction, respectively. Due to the slower freezing times used in the present study, the freeze damage may have been maximized during freezing resulting in minimal differences from the effect of temperature.

A study in beef semimembranosus showed that there was a significant interaction between freezing rate and storage temperature (Farouk, Wieliczko, & Merts, 2004).

These authors found that drip loss, sarcoplasmic protein solubility, and lightness were significantly affected by freezing rate with the slow frozen samples having lower quality (increased drip and lightness, decreased sarcoplasmic protein solubility). These differences were more pronounced early in the storage period but reduced as storage time increased. This result supports the above reasoning that our study did not find

93 significant differences due to the formation of large ice crystals during freezing, leaving little room for change during storage. These authors also found that storage temperature did not have a significant effect on the functional attribute of beef muscle; however, the temperatures studied ranged from −75 to −18°C, much lower than those explored in the current study.

Another study performed previously in chicken meat (breast and leg) found differences in protein extractability, free sulfhydryl content, and ATPase activity in meat stored at −4, −10, −18, and −80°C (Khan, Van Den Berg, & Lentz, 1962). This study looked only at very long storage times with their first time being 50 weeks (over

11 months) and the differences were not dramatic even at −4°C (over 40% retention of protein extractability) when compared to −80°C. This suggests that the minor differences observed in the relatively short storage period of the current study may be in line with previous studies on frozen chicken meat.

This study, along with evidence from the literature, suggests that the functionality of chicken meat proteins may not be significantly affected by frozen storage temperature, at least during shorter storage periods (Perez-Chabela et al., 2004). It has also been noted that when data from many different publications are plotted together there is significant scatter at any given temperature (James & James, 2012), which is reflected in the current data here. A number of factors may account for the variability found in the data, but these factors are representative of what the consumer may see even at the retail level. In addition to the functional analyses performed here, other studies including consumer acceptance panels and microbiological testing would need to be

performed to determine if elevated frozen storage temperatures are a viable alternative

for the storage of chicken meat products.

4.4 Conclusion

The in-depth analysis of the kinetics of myoglobin oxidation in a model system has provided some insight into the mechanisms of reverse stability for this system. The change in reaction rate follows typical Arrhenius kinetics with changes in temperature above the initial freezing temperature. Once frozen, there is an initial decrease in rate followed by an increase with a local maximum at about −20°C. The exploration of added

NaCl shows that the concentration of salts within the unfrozen aqueous fraction is contributing to the acceleration of oxidation, even in unfrozen solutions.

A model accounting for salt and temperature, extrapolated from unfrozen solutions does not account for the observed deviations in rate acceleration, indicating other factors are at play in frozen systems; these factors are most likely causing a change in the stability of the myoglobin protein structure resulting in an increase in oxidation. Again, noticing that pure myoglobin in deionized water does not display the phenomenon of reverse stability reinforces the role of dissolved solids in the mechanism of destabilization of the myoglobin protein.

While the results of the kinetic study on the oxidation of myoglobin in a model system indicate there may be a rate acceleration under frozen storage conditions, the data in the two meat systems did not demonstrate the same trend. Instead, for all of quality attributes investigated, there were no significant differences with storage temperature.

There are a number of factors that may be contributing to the small changes observed in

95 this study, as mentioned previously. However, based on the current data, it appears that frozen storage temperature does not significantly affect the physical quality of chicken meat, whether in the form of whole breasts or a ground, formulated product. There was also no significant difference due to freezing rate of the patties.

Though the current recommendations for frozen storage have been in place for decades, it is important to consider how changing practices and needs may trigger a new need to re-evaluate current practice. The present studies illustrate that the relationship between frozen storage temperature and product quality is not as simple as once thought.

There are many factors at play in frozen products, and we may be able to exploit these in order to create more efficient practices in the frozen food industry.

4.5 Tables and Figures

Table 4.1. Statistical evaluation of reaction order fit to quality attributes in frozen chicken meat products. These reaction orders are used to calculate the rate constants in Tables 3 and 4.

Whole Chicken Breast Chicken Patties (Still) Chicken Patties (Blast) Order SSE RSq Order SSE RSq Order SSE RSq Drip 0 0.011 0.096 ------MC 1/2 0.002 0.713 1/2 0.001 0.744 1/2 0.001 0.700 WHC 0 0.045 0.282 0 0.019 0.142 0 0.022 0.126 Cook loss 0 0.063 0.200 0 0.055 0.427 0 0.039 0.364

Table 4.2. Rate constants calculated for the change in quality attributes in whole chicken breasts.

Rate constants (k) x 104 −5°C −10°C −20°C Drip -1.44 -0.74 -1.60 MC 0.95 -0.28 -0.62 WHC 4.04 4.82 8.36 Cook loss -4.28 -9.98 -4.75

Table 4.3. Rate constants calculated for the change in quality attributes in ground chicken patties.

Rate constants (k) x 104 Still Air Frozen Blast Air Frozen −5°C −10°C −18°C −5°C −10°C −18°C MC 0.51 0.58 1.81 0.56 0.31 0.68 WHC 0.25 -2.20 0.05 -3.40 -2.34 -2.19 Cook loss -8.08 -7.57 -11.94 -6.29 -7.59 -8.91

T (°C) 20 15 10 5 0 -5 -10 -15 -20 -25 -30 -35 0 -1 A -2 -3

-4 ln(k) -5 y = -16070x + 54.084 -6 R² = 0.9885 y = -4E+07x2 + 297458x - 595.79 -7 R² = 0.9698 -8 0.0034 0.0035 0.0036 0.0037 0.0038 0.0039 0.0040 0.0041 1/T(K-1) T (°C) 20 15 10 5 0 -5 -10 -15 -20 -25 -30 -35 0 -1 B -2 -3

-4 ln(k) -5 -6 y = -17241x + 58.41 R² = 0.9137 y = -4E+07x2 + 328244x - 660.77 -7 R² = 0.9458 -8 0.0034 0.0035 0.0036 0.0037 0.0038 0.0039 0.0040 0.0041 1/T (K-1)

140

120

100 y = -23.987x + 121.52 80 R² = 0.8233

60 Ea Ea (kJ/mol) 40

0 0 0.5 1 1.5 % NaCl Added

Figure 4.2. Plot of activation energy of oxymyoglobin oxidation as a function of added NaCl at temperatures above the initial freezing temperature.

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1/T (K) 0.0033 0.0035 0.0037 0.0039 0.0041 0.0043 0 -1 -2 -3 -4 -5 ln (k) ln -6 -7 -8 Model from Data below Tf -9 Salt/T Model -10

Figure 4.3. Comparison of model using only elevated salt data from above freezing (solid line) to model created from full dataset with 0% added salt (dashed line).

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4.6 REFERENCES

AOAC International. (1995). AOAC Official Method 950.46 Moisture in Meat. In AOAC Official Methods of Analysis.

Barsa, C. S., Normand, M. D., & Peleg, M. (2012). On Models of the Temperature Effect on the Rate of Chemical Reactions and Biological Processes in Foods. Food Engineering Reviews, 4(4), 191–202.

Bekhit, A., & Faustman, C. (2005). Metmyoglobin reducing activity. Meat Science, 71, 407–439.

Bevilacqua, A. E., & Zaritzky, N. E. (1980). Ice morphology in frozen beef. Journal of Food Technology, 15(6), 589–597.

Bevilacqua, A., Zaritzky, N., & Calvelo, A. (1979). Histological measurements of ice in frozen beef. Journal of Food Technology, 14, 237–251.

Brown, W. D., & Dolev, A. (1963). Effect of Freezing on Autoxidation of Oxymyoglobin Solutions. Journal of Food Science, 28(2), 211–213.

Buttkus, H. (1967). The Reaction of Myosin with Malonaldehyde. Journal of Food Science, 32, 8–10.

Buttkus, H. (1970). Accelerated denaturation of myosin frozen solution . J Food Sci, 35(5), 558–562.

Finn, D. B. B. (1932). Denaturation of Proteins in Muscle Juice by Freezing. Proceedings of the Royal Society of London, 111(772), 396–411.

Franks, F. (1995). Protein destabilization at low temperatures. Advances in Protein Chemistry, 46, 105–139.

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Gotoh, T., & Shikama, K. (1974). Autoxidation of Native Oxymyoglobin from Bovine Heart Muscle. Archives of Biochemistry and Biophysics, 163, 476–481.

Heldman, D. (1974). Predicting the relationship between unfrozen water fraction and temperature during food freezing using freezing point depression. Trans. ASAE, 63– 66. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Predicting+the+R elationship+Between+Unfrozen+Water+Fraction+and+Temperature+During+Food+ Freezing+Using+Freezing+Point+Depression#0

Hessler, J. P. (1997). The use of Monte Carlo simulations to evaluate kinetic data and analytic approximations. International Journal of Chemical Kinetics, 29(11), 803– 817.

Huffman, K., Zapata, I., Reddish, J. M., Lilburn, M. S., & Wick, M. (2012). Feed restriction delays developmental fast skeletal muscle myosin heavy chain isoforms in turkey poults selected for differential growth. Poultry Science, 91(12), 3178–83.

Jul, M. (1982). The intracacies of the freezer chain. International Journal of Refrigeration, 5(4), 226–230.

Khan, A., Van Den Berg, L., & Lentz, C. P. (1962). Effects of Frozen Storage on Chicken Muscle Proteins. Journal of Food Science, 28(4), 425–430.

Kiessling, K. H. (1977). Muscle structure and function in the goose, quail, pheasant, guinea hen, and chicken. Comparative Biochemistry and Physiology -- Part B: Biochemistry and, 57(4), 287–292.

Lai, D.-J. (1984). Quantification of the Freeze-Concentration Effect on the Quality Degradation Rates During Frozen Food Storage. Michigan State University.

Lindeløv, F. (1978). Reactions in frozen foods; the reactions of myosin and single amino acids with some aldehydes. International Journal of Refrigeration.

Peleg, M., Engel, R., Gonzalez-Martinez, C., & Corradini, M. G. (2002). Non-Arrhenius and non-WLF kinetics in food systems. Journal of the Science of Food and Agriculture, 82(12), 1346–1355.

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Peleg, M., Normand, M. D., & Corradini, M. G. (2012). The Arrhenius equation revisited. Critical Reviews in Food Science and Nutrition, 52(9), 830–51.

Perez-Chabela, M., Mateo-Oyagüe, J., Pérez-Chabela, M. L., Mateo-Oyagüe, J., Hui, Y. H., Cornillon, P., … Mateo-Oyague, J. (2004). Frozen Meat: Quality and Shelf Life. In Y. H. Hui (Ed.), Handbook of Food Science, Technology, and Engineering (Vol. 3, pp. 201–214). Boca Raton, FL: CRC/Taylor & Francis.

Petracci, M., Sirri, F., Mazzoni, M., & Meluzzi, a. (2013). Comparison of breast muscle traits and meat quality characteristics in 2 commercial chicken hybrids. Poultry Science, 92(9), 2438–47.

Renerre, M., Anton, M., & Gatellier, P. (1992). Autoxidation of purified myoglobin from two bovine muscles. Meat Science, 32(3), 331–342.

United States Department of Agriculture (USDA). (1999). Appendix B: Compliance Guidelines for Cooling Heat-Treated Meat and Poultry Products (Stabilization). Retrieved January 1, 2016, from http://www.fsis.usda.gov/OPPDE/rdad/FRPubs/95- 033F/95-033F_Appendix B.htm

Updike, M. S., Zerby, H., Utrata, K. L., Lilburn, M., Kaletunc, G., & Wick, M. (2006). Proteins associated with thermally induced gelation of turkey breast meat. Journal of Food Science, 71(9).

Wang, W., & Roberts, C. J. (2013). Non-Arrhenius Protein Aggregation. The AAPS Journal, 15(3), 840–851.

Xiong, Y. L. (1997). Protein denaturation and functionality losses. Quality in Frozen Food, 111–139.

Zanoni, S., & Zavanella, L. (2012). Chilled or frozen? Decision strategies for sustainable food supply chains. International Journal of Production Economics, 140(2), 731– 736.

Zaritzky, N. E., & Martino, M. N. (1988). Ice Crystal Size Modifications during Frozen Beef Storage. Journal of Food Science, 53(6), 1631–1637.

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CHAPTER 5: Assessment of chicken meat quality after freeze/thaw cycling using

magnetic resonance imaging techniques

Abstract

The freezing and thawing of meat, as may happen in a temperature abuse scenario, can result in quality losses due to the formation, melting, and reformation of ice crystals.

These temperature fluctuations alter the way proteins interact with water within the highly-organized protein structure as well as the localization of water within the larger meat structure. The purpose of this research was to use magnetic resonance imaging

(MRI) and high-field micro-imaging to assess how localization and mobility of water affect the quality of chicken breast meat as a function of increasing freeze/thaw cycles.

Whole chicken breasts, either with or without a salt and phosphate brine, were frozen or thawed up to two consecutive times. After each freeze/thaw cycle, the quality of the chicken was measured in terms of drip loss, water holding capacity, brine uptake, cook loss, and shear texture. In addition, a separate set of chicken breasts was imaged using a

3T human MRI to collect T1-weighted and proton density images and T2 maps. T2 maps were also collected on small sections of unbrined samples using a high-field NMR with a micro-imaging probe. Drip loss increased significantly for unbrined chicken breasts after each freeze/thaw cycle and brine uptake decreased after two cycles. Drip loss for brined chicken breasts increased only after 2 cycles. All other measured attributes were not significantly affected. The brine helped protect against freeze/thaw abuse for drip loss 105 and brine uptake. Changes in localization of water after freeze/thaw cycles could be observed in the MRI images. The distributions of proton density in unbrined samples shifted after freeze/thaw abuse. MRI T2 distributions shifted slightly. Brined samples had less change after freeze/thaw abuse for both proton density and T2 distributions. High- field NMR micro-imaging showed greater change in T2 distributions. Quality attributes were assessed for correlation with each other as well as the T2 results. NMR bulk T2 values were strongly correlated with texture and cook loss.

5.1 Introduction

The freezing and subsequent thawing of meat can have significant negative impacts on the quality, including attributes such as moisture, protein denaturation, oxidation, color, and texture (Leygonie et al., 2012). One of the biggest causes of quality loss in frozen product is freeze/thaw abuse caused by large fluctuations in temperature, intentional or unintentional (Ali et al., 2015; Hansen et al., 2004; Jeong et al., 2011; Xia et al., 2009). These types of fluctuations may occur as a result of power failures, lack of proper cold chain management, or intentionally in the home kitchen if meat is thawed and refrozen. Whatever the case may be, the changes to meat quality could reduce the acceptability of the meat product in question.

The role of water in the structure and palatability of meat is not a new concept (Trout,

1988). Since freezing is a phenomenon dependent on the phase change of liquid water to solid ice, it follows that this transition may result in changes in how the meat, namely the proteins actin and myosin, interact with the water (Schnepf and Parsegian, 1992).

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Modifications to the structure of proteins can drastically change their functional properties.

Magnetic resonance imaging (MRI) is relatively novel in its applications to food science and technology. Studies have been performed using MRI to investigate the staling of bread with and without soy ingredients (Lodi et al., 2007), as a method of differentiating previously frozen trout (Foucat et al., 2001), and to explore changes in water mobility upon cooking of chicken meat (Shaarani et al., 2006) among others. These imaging techniques can provide quantitative data as well as qualitative images. We hypothesize that with increased freeze/thaw cycles there will be a decrease in quality, which will correlate with measurements from MRI images. The objective of this study was to use MRI and high-field NMR micro-imaging to determine the effect of freeze/thaw abuse on the water mobility and localization and how these methods predict the quality of chicken breast meat and give a better understanding of the mechanisms of quality loss in frozen meat.

5.2 Materials and Methods

5.2.1 Chicken meat samples.

Boneless, skinless chicken breasts were obtained from a local purveyor. Samples were not frozen prior to experimentation. Half of the samples were brined with a solution of 5.21% (w/w) NaCl and 3.45% (w/w) tripolyphosphate to a target of 15% (w/w) added weight and tumbled for 1 h at 4°C in a vacuum tumbler (Model LP-15, UltraSource USA,

Kansas City, MO). All samples were separately vacuum packaged and stored at 4°C

107 overnight prior to analysis. For MRI and functional analyses, whole chicken breasts were subjected to the freeze/thaw protocol described below.

Only unbrined chicken breast was used in micro-imaging experiments. Prior to analysis for micro-imaging, samples were excised from the whole muscle from the thickest portion of the breast and cut into roughly cylindrical segments of 3 cm long by 7 mm in diameter. The segments were wrapped gently in Teflon tape and inserted into glass shell tubes (8 mm by 4 cm) open at both ends. The ends were sealed with plastic caps and cyanoacrylate glue.

5.2.2 Freeze/thaw protocol.

For MRI, whole, intact chicken breasts were placed in a still air freezer at −20°C and allowed to freeze overnight. Frozen breasts where then thawed in 20°C water until an internal temperature of 4°C was reached (about 1 h). For micro-imaging, samples in tubes were frozen and thawed using a programmable, temperature-controlled bath to mimic the measured freeze/thaw profile at the center of the whole, intact chicken breasts frozen in for MRI analysis. Samples were cooled from 4°C to −20°C over 8 h, held for 6 hours at

−20°C, then thawed to 4°C over 1 h in water at 20°C. For functional analyses, different chicken breasts (n = 3) were analyzed after each treatment (0, 1, or 2 freeze/thaw cycles).

Preliminary testing showed that differences among chicken breasts were negligible and new samples could accurately represent each treatment.

5.2.3 Drip loss.

Drip loss was measured by mass difference after thawing. The weight before freezing was recorded. The meat was then frozen and thawed, as described above. After thawing,

108 the meat was removed from the package and re-weighed. Drip loss was calculated as the percentage of mass lost after thawing.

5.2.4 Water holding capacity.

WHC of each PM was determined with a modification of the method described by Li and Wick (2001). Briefly, approximately 500 mg of meat sample was placed between two Whatman No.1 filter papers. Filter papers are then placed between two Teflon® plates then placed between the plates of a Carver Press and subjected to a mechanical force of 2000 psi for 1 min. After compression, the filter papers were separated from the plates, and the images scanned. The compression produces two circles, an inner circle corresponding to meat film area and an outer circle corresponding to the total surface area. The % moisture of each sample was determined by air-drying method (100oC, 18 hours), described by AOAC (1995). Total moisture (mg) is calculated using the following equation:

Total moisture, mg = (Original meat weight, g) x (moisture %) x 1000 mg/g.

The areas of the two circles are determined by image analysis and the area between the two traced circles on the filter paper was defined as amount of free water in meat. The compression procedure was also employed to determine the wet area of 20 mg of water as a reference for free water (n = 5). The result from each sample is recorded, and WHC

(%) is calculated as follows:

WHC = (free water, mg / original meat weight, mg) x 100.

5.2.5 Brine uptake.

The amount of brine uptake was measured using a modification of the method

109 reported by Updike et al. (2005). Briefly, 2.5 g of chicken PM was minced with a razor blade and weighed into 15 ml conical centrifuge tubes in triplicate. The samples were mixed with 7.5 mL of brine (1.4M NaCl, 0.01 M Na tripolyphosphate, pH 7.6) (Li and

Wick, 2001). The mixture was held for 1 h at 4°C and then centrifuged at 3,500 × g for

30 min at 4°C (Sorvall Legend RT+, Thermo Scientific, Waltham, MA). The supernatant was transferred to a tared tube and the mass recorded. The amounts of brine absorbed by the pellet and remaining in the supernatant were calculated.

5.2.6 Cook loss.

Both brined and unbrined samples were cooked in vacuum bags submerged in a recirculating hot water bath at 80°C and cooked to a final internal temperature of 80°C

(Petracci et al., 2013). After cooking, chicken breasts were cooled down to 4°C following

Appendix B protocols (USDA, 1999). Temperatures were monitored with a type T thermocouple throughout cooking and cooling in an extra chicken breast that was not used in the analysis. The weight of each sample was measured before cooking (after addition of brine) and after cooking and cooling. The amount of cook loss was determined by the mass difference between the two weights.

5.2.7 Texture.

Texture was measured using a blunt Meullenet-Owens razor shear (BMORS) method as described by Lee, Owens, & Meullenet (2008). Briefly, cooked chicken breasts were penetrated by a blunt blade attachment on a TA-XTplus texture analyzer (Texture

Technologies Corp, Hamilton, MA) with a pre-test crosshead speed of 2 mm/s, test and post-test crosshead speed of 10 mm/s and penetration depth of 12 mm with a trigger force

110 of 10 g. A total of 4 predetermined locations were measured on each chicken breast sample. The maximum force and total shear energy were recorded.

5.2.8 NMR Micro-imaging.

Imaging was performed on a Bruker Ascend 800 NMR equipped with a 10 mm

Micro5 imaging probe (Bruker, Billerica, MA). T2 mapping was performed with a multi- spin, multi-echo image acquisition protocol. T2 acquisition was accomplished using 16 echo images spaced from 5 to 80 ms echo times (TE) with 5 ms echo spacing, 2.2 s repetition time (TR), 8 averages, 7 axial slices with 2 mm slice thickness, image sixe 200 x 200, FOV 8 mm x 8 mm, resolution of 40 microns. T2 mapping uses the images acquired at different echo times to construct a T2 decay curve at each pixel resulting in an image with pixel values equivalent to the T2 at that location so brighter pixels have a longer T2 and darker pixels have a shorter T2.

5.2.9 Magnetic resonance imaging.

Magnetic resonance (MR) images were acquired using a 3T Philips Ingenia human scanner (Cleveland, Ohio). A transmit/receive 16 channel knee RF (radio frequency) resonator was selected for the image acquisition since its imaging volume was most appropriate. The MRI measurement was performed at room temperature.

Sagittal images were acquired including a high resolution T1 weighted three- dimension (3D) magnetization-prepared rapid gradient-echo (MPRAGE) sequence with a field of view (FOV) 180 × 140 × 100 mm, TR/TE/TI 6.2/2.7/900 ms, voxel resolution

1.00 × 1.25 × 1.00 mm, and flip angle 10°. T1 weighted images use scanning parameters with short TR and short TE to minimize the effects of T2 relaxation. As a result, protons

111 with a short T1 (such as fat) appear bright and those with long T1 (such as water) appear dark. A proton density images with FOV 180 × 120 × 95 mm, voxel resolution 1 × 1 × 1 mm, TR/TE 16/7.2 ms, flip angle 5°. A turbo spin echo was acquired to measure T2 with

FOV 180 × 119 × 95 mm, voxel resolution 0.90 × 1.12 × 2.00 mm, TR 1970 ms, nine echoes were acquired with multiples echo times of 12 ms. Proton density images are dependent on the total number of excitable protons in a sample, regardless of relaxation times, resulting in areas with a high concentration of protons appearing bright and areas with fewer protons appearing darker.

5.2.10 Statistical analysis.

Differences in the means of the various measured quality attributes was determined by a one-way ANOVA run in JMP 12.0 statistical software (SAS Institute Inc., Cary,

NC). Means were considered significantly different at α = 0.05. MRI T2 maps were converted into pixel-wise T2 values using an in-house program executed using IDL software (Harris Corporation, Melbourne, FL). Three slices were analyzed from the center of each of three chicken breasts under each condition. Histograms were generated for each sample in Excel (Microsoft Corporation, Redmond, WA) and average histograms calculated based on three samples for each condition. For MRI T2 distributions, the full width at half max (FWHM) was measured by approximating the histogram as a normal distribution and calculating the FWHM from the parameters of the fit (code in Appendix B: SAS code for fitting of normal distributions to T2 distributions from MRI image analysis).

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5.3 Results and Discussion

5.3.1 Quality measurements.

A number of attributes of chicken meat were measured in both unbrined and brined chicken breasts to assess how multiple freeze/thaw cycles would affect the quality of the meat. Chicken meat is often vacuum tumbled with brine, causing an up to 15% increase in the original meat weight. This is also referred to as “enhanced” meat and is done to increase the water binding of the meat proteins as well as create a more tender meat product. We tested the effect of freeze/thaw abuse on unbrined and brined chicken breast to determine if and how much cryoprotection brining before freezing might afford.

Drip loss, or more appropriately thaw loss in frozen and thawed meat, is one of the most commonly measure quality attributes in meat. This water loss from the meat can represent a significant commercial loss for meat processors. In the current study, there was no significant difference between unbrined and brined chicken breasts at any number of freeze/thaw cycles (Figure 5.1). However, in the unbrined samples, there was an increase in the amount of drip loss after 1 and again after 2 freeze/thaw cycles while the brined samples had a significant increase only after 2 freeze thaw cycles. This shows that the brine afforded some level of protection from the freeze/thaw abuse.

The WHC of brined chicken breast was significantly higher than that of unbrined chicken in the unfrozen samples (Figure 5.2). However, after this difference is not present after 1 freeze/thaw cycle and returns after 2 cycles. These differences are also quite small and with the lack of a trend, this may not be an appropriate measure for assessing the effect of freezing on chicken meat. Similarly, there is not a strong trend in

113 the ability of the meat to take up brine (Figure 5.3). There is no significant difference between unbrined and brined samples until after 2 freeze/thaw cycles. The unbrined samples have differences across the number of cycles, but not in any predictable manner while the brined samples have no difference regardless of number of cycles.

Cook loss, the amount of water expelled from the meat upon cooking, was also not affected by the number of freeze/thaw cycles for either unbrined or brined samples

(Figure 5.4). The brined samples, however, had lower cook loss than unbrined samples at each point. This is to be expected since the function of the brine it to help retain moisture and make for a juicier end product. This is also represented in the texture analysis

(Figure 5.5) where the unfrozen brined samples had lower force and energy, corresponding to higher tenderness, than the unbrined samples. As with the WHC, the difference between unbrined and brined is lost after just 1 freeze/thaw cycle. Likewise, there is no difference across number of cycles within each breast type.

It has been pointed out that there are few studies which show that the freezing method or rate of freezing has a significant impact on the final eating quality of meat, but may impact other quality attributes such as drip loss, which is consistent with the results of the current study (James and James, 2012).

5.3.2 MRI imaging.

MRI was used to determine if large scale changes in water mobility within the chicken breasts were occurring with increased freeze/thaw abuse. Figure 5.6 shows representative T1-weighted images acquired for unbrined and brined chicken breasts subjected to increasing freeze/thaw cycles. In these images, water appears dark and the

114 signal intensities are inversely proportional to the mobility of water molecules. These images do not give us quantitative information but allow us to make qualitative conclusions. In the unfrozen samples, we can see that the brined chicken breasts have more pockets of intracellular water. This is due to the addition of water in the form of brine, which causes swelling of the muscle fibers and uptake of this water. After the first freeze/thaw cycle, we can see that the unbrined sample has a slight increase in these areas of high-mobility water, indicating that the freeze/thaw process has caused previously entrapped water to escape into the intracellular space. This may be the contributing population to the measured increase in drip loss. The brined samples however exhibit minimal change. This is also consistent with the drip loss data collected. With increased freeze/thaw cycles more of this highly-mobile water can be seen in the brined samples.

Figure 5.7 shows representative proton density images for the chicken breast samples. In these proton density images, areas with a high concentration of water appear bright. These images allow a semi-quantitative assessment of the amount of water within the chicken breast. The relative intensity of pixels within the samples was measured and is presented in Figure 5.8 as histograms. The unfrozen, unbrined sample shows the highest average signal intensity. After the first freeze/thaw cycle, the intensity decreases due to loss of water from the meat in the form of drip loss, as confirmed by the measurements of drip loss directly. The brined samples started out with less homogeneity in proton density. This was likely due to the manner in which the brine was absorbed into the meat structure. In fact, two populations of protons can be observed from the distribution, suggesting that the brine and sarcoplasm were not co-mingling but were

115 present in two distinct populations. After the first freeze/thaw cycle, there was a large shift to lower intensity and a more homogeneous water population. This may be an anomaly, but could also be due to a redistribution of water within the meat structure.

After the second freeze/thaw cycle, the distribution was more similar to the unfrozen samples, but the lower intensity population had shifted closer to the higher. Again, this is likely due to a redistribution of water within the system.

T2 mapping was also performed to determine quantitative changes in the water mobility of the system. This pulse sequence allows for pixel-by-pixel calculation of T2 values so quantitative data can be extracted from the acquired images. A representative image from each condition is presented in Figure 5.9. The unbrined samples showed some areas of higher mobility within the meat structure in the unfrozen samples. These areas appear to increase somewhat after freezing and thawing. In the brined samples, the areas of high mobility appeared to be spread more uniformly throughout the entirety of the breast. The brined samples also showed little change after freezing and thawing. The

T2 maps were also converted to distributions to make a quantitative assessment of the homogeneity of water mobility (Figure 5.10). Both the unbrined and brined samples show a fairly homogeneous population of T2 values in the unfrozen samples. In the unbrined samples, the freeze/thaw cycles result in a slight shift to lower T2 values and a higher tail at the higher values. This may be due to the loss of water from the system, resulting in a change in the protein-water interactions responsible for water mobility. The brined samples show less change as a function of freeze/thaw cycle.

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Previous studies have used MRI to quantify changes in the T2 distributions of chicken breasts as a function of cook time (Shaarani et al., 2006). This study found dramatic shifts in the T2 distributions as the cook time was increased with a shift toward lower T2 values, corresponding with the modification of the protein structure and expulsion of water upon heating of the meat. Cooking is an extreme process in comparison to freezing and thawing, which could explain the small changes observed in the current study. A similar study performed on the authentication of freeze/thaw abuse in fresh pork also found relatively small (~10%) changes in bulk T2 values after one freeze/thaw cycle and even smaller changes after a second cycle (Guiheneuf et al., 1997). The effect of freezing and thawing on other magnetization parameters has been presented for multiple species, including beef, lamb, pork, chicken, turkey, and trout, and minimal changes were observed in each species (Hall et al., 1998). The authors attribute this primarily to inter- animal variation and determined that the use of MR properties in the authentication of freeze/thaw abuse on meat has limited application because of this variation. The current study was able to determine some changes within chicken breast meat as a function of freezing and thawing as well as a function of brining. Though MRI may not be applicable to authentication, it may prove valuable in a research setting.

5.3.3 NMR Micro-imaging.

In addition to MRI, NMR micro-imaging was used to measure changes in the mobility and localization of water based on T2 mapping. This method uses the same concepts as MRI but on a much smaller scale. This method also utilizes a much stronger magnetic field, allowing for better signal separation and higher resolution. An example of

117 the acquired images is presented in Figure 5.11. Much finer structure can be seen in these images as compared to the MRI images. Again, the T2 maps were analyzed by creating T2 distributions from the images. It can be seen in Figure 5.12 that the unfrozen chicken breast has a uniform population of T2 values. After 1 freeze/thaw cycle the peak begins to broaden and shift to higher T2 values indicating a less homogeneous population of higher-mobility water molecules. After 2 freeze/thaw cycles, the peak is very broad with the average shifted significantly higher, again indicating less homogeneity and higher mobility. These observations are not consistent with what was observed in the

MRI T2 maps but do correlate with the increase in drip loss observed in the unbrined samples. The small and controlled nature of the sample may account for differences in T2 distributions found with micro-imaging as opposed to those found in MRI. This method also used a much higher field strength magnet (800 MHz vs 170 MHz) which allows for higher sensitivity. The use of T2 distributions is useful in understanding the changes in water mobility beyond bulk T2 measurements (Table 5.1), which show only small changes as a function of freezing and thawing.

NMR micro-imaging is a relatively new technique in terms of its application to foods.

Previous studies have used this methodology to examine changes in the structure of rabbit meat post-mortem (Bertram et al., 2004a) and differences in beef ageing with and without high pressure treatment (Bertram et al., 2004b). Both of these studies have focused on T2 mapping due to the body of research suggesting T2 as one of the most telling magnetic resonance properties affected by protein structure in meat (Renou et al.,

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2003). This study illustrates that this technique may be used in the assessment of how freezing processes affect the water mobility in meat.

5.3.4 Correlation of quality attributes and magnetic resonance measurements

In order to determine if any of the measured attributes of the chicken breast meat were correlated, a correlation matrix was generated (Table 5.2). Based on this analysis we can see that some of the quality parameters are correlated with each other, as well as with the measurements from the NMR micro-imaging. Cook loss was significantly correlated with the WHC by press, the two measures of texture, and the bulk T2. Texture and cook loss are both influenced by the cooking time and temperature, so it is expected that these attributes would be linked. The WHC was also correlated with the moisture content and brine uptake. These measures, including cook loss, are indicators of how well the proteins in the meat can hold water in various ways. The two texture measurements are also correlated with the bulk T2 measurement. Based on this analysis, it is clear that the high-field NMR micro-imaging on raw chicken breast meat may be useful in predicting the final product quality in terms of cook loss and texture.

5.4 Conclusion

Freezing and thawing had minimal impact on many of the quality attributes measured in this study. Only drip loss was significantly impacted by increased freeze/thaw cycles.

MRI images were useful in assessing the changes in water distribution and populations and the relationship with the change in drip loss. T1-weighted images illustrated the changes in water localization and the effect of brine in helping protect the meat from freeze/thaw abuse. Proton density images also aided in assessment of how the

119 freeze/thaw process induced changes in the populations of water present in both unbrined and brined samples. Unbrined chicken breasts exhibited a homogeneous water population in all conditions, but the loss of water as drip was evident in the shift to lower intensities.

Brined samples showed less change, which corresponds to the smaller changes in drip loss. MRI T2 maps showed that the unbrined samples experienced a slight shift in T2 distributions and the brined samples showed even less difference. However, use of NMR micro-imaging showed that on a microscopic level, there are shifts in T2 populations as a function of freeze/thaw toward a less homogeneous population with higher mobility, indicating that freezing and thawing has caused a change in the water binding. Quality parameters associated with cooking (cook loss and texture) were strongly correlated with each other, but also with bulk T2 measurements from NMR micro-imaging. WHC was somewhat correlated with other measures of water binding in meat proteins including moisture content, cook loss, and brine uptake.

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5.5 TABLES and FIGURES

Table 5.1. Average T2 values extracted from MRI and NMR micro-imaging T2 maps.

Sample T2 (ms) ± Std. Dev. Type Instrument Unfrozen 1 Freeze 2 Freezes MRI 62.36 ± 2.31 64.41 ± 1.76 62.99 ± 1.47 Unbrined NMR 17.85 ± 0.04 17.68 ± 0.61 18.16 ± 0.58 Brined MRI 63.41 ± 4.91 62.74 ± 2.37 63.84 ± 2.75

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Table 5.2. Correlation matrix for measured quality attributes and magnetic resonance parameters.

Pearson correlation coefficient Probability > |r|

1 Drip Brine Press Shear Shear MRI NMR MRI T2 MC 2 loss uptake WHC Force Energy Bulk T2 Bulk T2 FWHM Cook -0.4506 -0.2617 -0.3276 0.4941 0.8546 0.8424 -0.1784 -0.7681 0.1644 loss 0.0606 0.2943 0.1844 0.03713 <.0001 <.0001 0.4789 0.026 0.5144

MC 1 -0.0037 0.3686 -0.5853 -0.3608 -0.3855 0.0518 0.1497 -0.0973 <.0001 0.9885 0.1323 0.0107 0.1413 0.1141 0.8382 0.7234 0.701 Drip 1 -0.4164 -0.0249 -0.3932 -0.4191 0.1475 0.2691 0.0173 loss <.0001 0.0857 0.9219 0.1064 0.0834 0.5593 0.5193 0.9457 Brine 1 -0.6329 -0.1531 -0.1685 -0.1728 0.1833 -0.0998 uptake <.0001 0.0048 0.5443 0.5039 0.493 0.664 0.6935 Press 1 0.3699 0.4035 0.0304 -0.2284 -0.1571 WHC <.0001 0.1309 0.0968 0.9047 0.5864 0.5335 Shear 1 0.9856 -0.15 -0.8868 0.0945 Force <.0001 <.0001 0.5525 0.0033 0.709 Shear 1 -0.1238 -0.8556 0.0384 Energy <.0001 0.6246 0.0067 0.8799 MRI 1 0.0151 0.4425 Bulk T2 <.0001 0.9717 0.066 NMR 1 0.0381 Bulk T2 <.0001 0.9287 1Total moisture content (w.b.); 2Full width at half max (FWHM) of the T2 distribution from MRI; 3Values in bold indicate a significant correlation at P < 0.05

122

6% d d 5% c 4% b,c 3% a,b

2% a Mass Mass Loss (% mass) total 1%

0% Unfrozen 1 Freeze 2 Freezes

Unbrined Brined

Figure 5.1. Changes in drip loss in unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05).

123

Unfrozen1 Freeze2 Freezes 100% b,c a,b,c c c a a,b 80% Unbrined Expressed 60% Unbrined Held

Brined Expressed 40%

% Total % Moisture Brined Held 20%

0% Unfrozen 1 Freeze 2 Freezes

124

Unfrozen1 Freeze2 Freezes 100%

80% Unbrined Supernatant b b b Unbrined Pellet 60% b a,b Brined 40% Supernatant a

% Brine % Retained Brined Pellet 20%

0% Unfrozen 1 Freeze 2 Freezes

Figure 5.3. Brine uptake by unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05).

125

35% c c 30% b,c a,b 25% a,b a 20%

15%

10%

Cook loss Cook loss (% thawedmass) 5%

0% Unfrozen 1 Freeze 2 Freezes

Unbrined Brined

Figure 5.4. Cook loss from unbrined and brined chicken breasts after multiple freeze/thaw cycles. Means with different letters are significantly different (P < 0.05).

126

16 b,c A 14 c 12 a,b,c a,b,c a,b 10 a 8

6 Shear Force ShearForce (N) 4

0 Unfrozen 1 Freeze 2 Freezes

Unbrined Brined

120 b,c B c 100 a,b,c a,b,c a,b 80 a

40 ShearEnergy (N*mm) 20

0 Unfrozen 1 Freeze 2 Freezes

Unbrined Brined

127

Figure 5.6. T1-weighted images from unbrined and brined chicken breasts after 0, 1, and 2 subsequent freeze/thaw cycles.

128

Figure 5.7. Proton density images from unbrined and brined chicken breasts after 0, 1, and 2 subsequent freeze/thaw cycles.

129

Unbrined 10%

6% Unfrozen 4% 1 Freeze

% of % TotalPixels 2 Freezes 2%

0% 200 400 600 800 1000 1200 Pixel Intensity

Brined 10%

6% Unfrozen 4% 1 Freeze

% of % TotalPixels 2 Freezes 2%

0% 200 400 600 800 1000 1200 Pixel Intensity

Figure 5.8. Distributions of pixel intensity from proton density images of unbrined (A) and brined (B) chicken breasts subjected to 0, 1, or 2 freeze/thaw cycles.

130

Figure 5.9. T2 mapping images from unbrined and brined chicken breasts after 0, 1, and 2 subsequent freeze/thaw cycles.

131

10% Unbrined

6% Unfrozen 4% 1 Freeze

% of % pixels total 2 Freezes 2%

0% 40 50 60 70 80 90 100 110 120 T2 (ms)

10% Brined

Unfrozen 4%

1 Freeze % of % pixels total 2 Freezes 2%

0% 40 50 60 70 80 90 100 110 120 T2 (ms)

Figure 5.10. T2 distributions from T2 maps of unbrined (A) and brined (B) chicken breasts subjected to 0, 1, or 2 freeze/thaw cycles.

132

Figure 5.11. Proton density images for unbrined chicken breast samples subjected to 0 (A), 1 (B) or 2 (C) freeze/thaw cycles. Higher signal intensity is indicative of higher mobility water. Gaps appearing in images are indicative of micro-channels forming between muscle cells.

133

18 16 14 12 10 8 Unfrozen

% of % pixels 1 Freeze 6 2 Freezes 4 2 0 15 17 19 21 23 25 27 29 T2 (ms)

Figure 5.12. Average T2 distributions obtained from T2 maps of unbrined chicken meat samples using NMR micro-imaging.

134

5.6 REFERENCES

Ali, S., Zhang, W., Rajput, N., Khan, M.A., Li, C., Zhou, G., 2015. Effect of multiple freeze–thaw cycles on the quality of chicken breast meat. Food Chem. 173, 808– 814.

AOAC International, 1995. AOAC Official Method 950.46 Moisture in Meat, in: AOAC Official Methods of Analysis.

Bertram, H.C., Whittaker, A.K., Andersen, H.J., Karlsson, A.H., 2004a. Visualization of drip channels in meat using NMR microimaging. Meat Sci. 68, 667–670.

Bertram, H.C., Whittaker, A.K., Shorthose, W.R., Andersen, H.J., Karlsson, A.H., 2004b. Water characteristics in cooked beef as influenced by ageing and high-pressure treatment - An NMR micro imaging study. Meat Sci. 66, 301–306.

Foucat, L., Taylor, R., Labas, R., Renou, J., 2001. Characterization of frozen fish by NMR imaging and histology. Am. Lab. 33, 38–43.

Guiheneuf, T.M., Parker, A.D., Tessier, J.J., Hall, L.D., 1997. Authentication of the effect of freezing/thawing of pork by quantitative magnetic resonance imaging. Magn. Reson. Chem. 35, S112–S118.

Hall, L.D., Evans, S.D., Nott, K.P., 1998. Measurement of textural changes of food by MRI relaxometry. Magn. Reson. Imaging 16, 485–492.

Hansen, E., Juncher, D., Henckel, P., Karlsson, A., Bertelsen, G., Skibsted, L.H., 2004. Oxidative stability of chilled pork chops following long term freeze storage. Meat Sci. 68, 479–484.

James, S.S., James, C., 2012. Quality and Safety of Frozen Meat and Meat Products, in: Sun, D.-W. (Ed.), Handbook of Frozen Food Processing and Packaging. CRC Press, London, pp. 303–323.

Jeong, J.-Y., Kim, G.-D., Yang, H.-S., Joo, S.-T., 2011. Effect of freeze–thaw cycles on physicochemical properties and color stability of beef semimembranosus muscle. Food Res. Int. 44, 3222–3228.

Lee, Y.S., Owens, C.M., Meullenet, J.F., 2008. The Meullenet-Owens Razor Shear (MORS) for redicting poultry meat tenderness: Its applications and optimization. J. Texture Stud. 39, 655–672.

Leygonie, C., Britz, T.J., Hoffman, L.C., 2012. Impact of freezing and thawing on the quality of meat: review. Meat Sci. 91, 93–8.

Li, C.T., Wick, M., 2001. Improvement of the physicochemical properties of pale soft 135

and exudative (PSE) pork meat products with an extract from mechanically deboned turkey meat (MDTM). Meat Sci. 58, 189–195.

Lodi, A., Abduljalil, A.M., Vodovotz, Y., 2007. Characterization of water distribution in bread during storage using magnetic resonance imaging. Magn. Reson. Imaging 25, 1449–58.

Petracci, M., Sirri, F., Mazzoni, M., Meluzzi, a, 2013. Comparison of breast muscle traits and meat quality characteristics in 2 commercial chicken hybrids. Poult. Sci. 92, 2438–47.

Renou, J.P., Foucat, L., Bonny, J.M., 2003. Magnetic resonance imaging studies of water interactions in meat. Food Chem. 82, 35–39.

Schnepf, M., Parsegian, V.A., 1992. Protein-water interactions, in: Hudson, B. (Ed.), Biochemistry of Food Proteins. Springer Science & Business Media, pp. 1–33.

Shaarani, S.M., Nott, K.P., Hall, L.D., 2006. Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat. Meat Sci. 72, 398–403.

Trout, G.R., 1988. Techniques for measuring water-binding capacity in muscle foods-A review of methodology. Meat Sci. 23, 235–252.

United States Department of Agriculture (USDA), 1999. Appendix B: Compliance Guidelines for Cooling Heat-Treated Meat and Poultry Products (Stabilization) [WWW Document]. URL http://www.fsis.usda.gov/OPPDE/rdad/FRPubs/95- 033F/95-033F_Appendix B.htm (accessed 1.1.16).

Updike, M.S., Zerby, H.N., Sawdy, J.C., Lilburn, M.S., Kaletunc, G., Wick, M.P., 2005. Turkey breast meat functionality differences among turkeys selected for body weight and/or breast yield. Meat Sci. 71, 706–712.

Xia, X., Kong, B., Liu, Q., Liu, J., 2009. Physicochemical change and protein oxidation in porcine longissimus dorsi as influenced by different freeze-thaw cycles. Meat Sci. 83, 239–245.

136

CHAPTER 6: Conclusion

Based on the data presented in this work, it would appear that the physical quality of chicken meat is resilient to the freezing process with the exception of drip loss and gel forming ability. Freezing rate, one of the most important factors in frozen food quality, was found to cause changes in the gelation ability of the salt soluble proteins. Other quality attributes, such as moisture content, brine uptake, water holding capacity, and myofibrillar fragmentation index, we not significantly affected by freezing rate. Analysis of the myofibrillar fraction indicated an increase in total enthalpy and an increased contribution of sarcoplasmic proteins after freezing, regardless of rate. The results suggest that freezing rate causes a modification of the myosin head groups, resulting in a change of gelation behavior and final gel strength.

The impact of frozen storage temperature was explored in a model system as well as in whole meat systems. The results from the model system showed that the oxidation of myoglobin exhibited reverse stability with decreasing frozen storage temperature. This effect was magnified by the addition of NaCl to the extract, suggesting the ionic effects of salt are destabilizing the system, resulting in accelerated rates of oxidation. These results in a model system did not apply to whole chicken products. In whole chicken breasts and ground chicken patties, temperature did not significantly affect the rate of quality loss in terms of drip loss, color, moisture content, or water holding capacity or

137 cook loss. The freezing rate of the patties also did not affect these quality attributes over time.

The impact of freeze/thaw abuse on the quality of chicken breasts was assessed using magnetic resonance imaging techniques. Drip loss was the only measured attribute that showed significant change with increased freeze/thaw cycles. Physical distribution of water within the chicken breasts was observed in unbrined samples as freeze/thaw cycles increased. There were significant shifts in the proton density distributions from MRI images. Differences were not as pronounced in brined samples, suggesting a level of cryoprotection conferred by the brine. In both brined and unbrined samples, only small differences in T2 distributions were observed with MRI. Using NMR micro-imaging significant shifts in T2 distributions were observed in unbrined samples.

138

CHAPTER 7: Future Work

7.1 Freezing rate

Continued explorations into the effect of freezing rate on the quality of meat and meat products is needed to determine the mechanisms behind the observed differences in quality, namely the ability for meat proteins to form a gel upon heating. SDS-PAGE could be used to determine if different proteins or protein fragments are present in samples frozen at different rates. These may be part of the reason for the observed differences in gelation of the salt soluble proteins. Additionally, molecular dynamics modeling could be implemented to further explore the changes in the protein-water interactions during freezing at different rates. This may help elucidate the impact of ice crystal size on the structural changes of myosin.

7.2 Frozen storage temperature

The studies presented here show discrepancies in the effect of frozen storage temperature on meat quality. In the model system, reverse stability was observed at lower storage temperatures. The exact mechanism of this phenomenon is not clear. Further work could be performed to determine the mechanism such as modification of the model system with various components involved in the metmyoglobin reducing system. This mechanism may be of interest from a more basic point of view, but in practicality it does not seem as relevant in whole meat products, based on the data presented here. More

139 work could be done to determine the effects of frozen storage temperature on meat quality. The small changes observed here could be a function of the short storage time. A longer study could be performed to see if the lack of temperature impact is consistent at longer times. Additional studies would also need to consider other quality attributes, particularly microbial quality and lipid oxidation. These other attributes likely are not as resistant to temperature change as the physical quality parameters measured in the present study.

7.3 Chicken quality and magnetic resonance techniques

MRI has shown to be useful in the assessment of bulk properties of whole chicken breasts. This technique could be used in future studies on the effects of processing on chicken, and potentially other meats. Studies could explore the impact of freezing on other muscle groups. The complex interactions between different species can be explored by performing an MRI analysis on the composite food known as the turducken. The changes in water binding as a function of frozen storage could also be explored using magnetic resonance methods. The condition of woody breast, a wide-spread defect affecting the quality of chicken breasts in particular, could also be assessed using these techniques.

140

Appendix A: SAS code for Monte Carlo simulation of kinetic data

*This code reads in a dataset in a particular format and performs an analysis to get k values and the corresponding EA value. Standard errors and 95% confidence intervals are created via Monte Carlo.

For the appropriate dataset format, see "meat kinetics test.csv" - There must be a column called "time" in all lower-case letters - There must be temperature colums with labels like "temp10" or "temp20". The important thing is that the first four letters are "temp" - The measured values of the response must be in the column immediately following the corresonding temperature measurements. - Any number of temperatures can be used.

Results are printed to the results viewer using a PROC PRINT statement. The results are also stores in a SAS dataset "summary.sas7bdat" in the work directory.; proc import datafile = "C:\Users\phinney.14\Documents\My Box Files\Paul Park_Phinney\Copy of Sanitizer Biofilm Experiments(2) (3).csv" out = rawdata replace; run;

*Get the variable name information into a dataset; proc contents data = rawdata noprint varnum out = varnames ; run;

*Sort by the order in the raw data; proc sort data = varnames; by varnum; run; data temps values; retain totemp; set varnames; if _n_ = 1 then totemp = 1;

141

if totemp = 0 then do; output values; totemp = 1; end; if (substr(upcase(name), 1, 4) = "TEMP") then do; totemp = 0; output temps; end; run;

*Put temperature and corresponding value names together.; data temps; set temps; rename name = tempnames; keep name; run; data values; set values; rename name = valuenames; keep name; run; data names; merge temps values; run;

%macro looptemps();

data names; set names; one = 1; run;

proc means data = names noprint; var one; output out = num_names(drop = _type_ _freq_) sum = num_names; run;

data _NULL_; set num_names; call symput("num_names", trim(left(num_names))); run;

%do i = 1 %to &num_names.;

data thisgroup; set names;

142

if _n_ = &i.; run;

data _NULL_; set thisgroup; call symput("thisvalue", trim(left(valuenames))); call symput("thistemp", trim(left(tempnames))); run;

data temp1; set rawdata; rename &thisvalue. = value; rename &thistemp. = temp; keep time &thistemp. &thisvalue.; run;

data alldata; set alldata temp1; run;

%end;

%mend; data alldata; delete; run;

%looptemps();

*Rename this rawdata; data rawdata; set alldata; if value ^= .; run; proc datasets; delete alldata names num_names temp1 temps thisgroup values varnames; run;

*Calculate SD within each time and temperature on log scale.; proc sort data = rawdata; by time temp; run; data rawdata; set rawdata; logvalue = log(value); run;

143 proc means data = rawdata noprint; by time temp; var logvalue; output out = sdinfo(drop = _type_ _freq_) std = sigma; run; data rawdata; merge rawdata sdinfo; by time temp; run;

*Create simulated datasets; data simdata; set rawdata; do i = 1 to 10000; delta = rannor(0); simlogvalue = logvalue + sigma * delta; output; end; run;

*Fit the model within each of the datasets.; proc sort data = simdata; by i temp; run;

*Fit logvalue against time within each dataset and temperature.; proc reg data = simdata outest = ParameterEstimates noprint; by i temp; model simlogvalue = time; run; quit;

*Get mean, SD, and 95% values for the slopes within each temperature.; proc sort data = ParameterEstimates; by temp; run; proc means data = ParameterEstimates noprint; by temp; var time; output out = ksummary(drop = _type_ _freq_) mean = mean std = std; run; data ksummary; set ksummary; length variable $50.; variable = "k for temperature " || trim(left(temp)); mean = abs(mean); lower95 = mean - 1.96 * std; upper95 = mean + 1.96 * std;

144

note = "k_mean is the absolute value of the raw slope value."; drop temp; run;

*Fit logabsslope against 1 / (temp + 273); data ParameterEstimates; set ParameterEstimates; invabstemp = 1 / (temp + 273); logabsslope = log(abs(time)); keep i invabstemp logabsslope; run; proc sort data = ParameterEstimates; by i; run; proc reg data = ParameterEstimates outest = EaSim noprint; by i; model logabsslope = invabstemp; run; quit;

*Clean up the final result; data EaSim; set EaSim; invabstemp = -invabstemp * 8.314 / 1000; rename invabstemp = ea; keep invabstemp; run;

*Get the summary information.; proc means data = EaSim noprint; var ea; output out = summary(drop = _type_ _freq_) mean = mean std = std; run; data summary; set summary; length variable $50.; variable = "ea"; lower95 = mean - 1.96 * std; upper95 = mean + 1.96 * std; run; data summary; retain variable mean std lower95 upper95 note; set ksummary summary; run;

145 proc print data = summary; run;

146

Appendix B: SAS code for fitting of normal distributions to T2 distributions from

MRI image analysis

/* ------Code generated by SAS Task

Generated on: Monday, February 06, 2017 at 11:30:22 AM By task: BF01

Input Data: Local:WORK.T2_HISTOGRAMS Server: Local ------*/

%_eg_conditional_dropds(WORK.SORTTempTableSorted, WORK.TMP0TempTableForPlots); /* ------Sort data set Local:WORK.T2_HISTOGRAMS ------*/

PROC SQL; CREATE VIEW WORK.SORTTempTableSorted AS SELECT T."Freq BFT01"n, T."Bin BFT01"n FROM WORK.T2_HISTOGRAMS as T ; QUIT; TITLE; TITLE1 "Nonlinear Regression"; TITLE2 "Results"; FOOTNOTE; FOOTNOTE1 "Generated by the SAS System (&_SASSERVERNAME, &SYSSCPL) on %TRIM(%QSYSFUNC(DATE(), NLDATE20.)) at %TRIM(%SYSFUNC(TIME(), TIMEAMPM12.))"; PROC NLIN DATA=WORK.SORTTempTableSorted MAXITER=100 CONVERGE=1E-05 SINGULAR=1E-08 MAXSUBIT=30 147

NOITPRINT ; MODEL "Freq BFT01"n = exp( a*"Bin BFT01"n*"Bin BFT01"n + b*"Bin BFT01"n +c) ; PARMS a=-0.02 b = 3 c = -80 ;

OUTPUT OUT=WORK.TMP0TempTableForPlots H=_h1 L95M=_l95m L95=_l95 PARMS=a PREDICTED=_predicted1 RESIDUAL=_residual1 SSE=_sse1 STDI=_stdi1 STDP=_stdp1 STDR=_stdr1 STUDENT=_rstudent1 U95M=_u95m U95=_u95 WEIGHT=_weight1 ; RUN;

TITLE; TITLE1 "Regression Analysis Plots"; PROC SORT DATA=WORK.TMP0TempTableForPlots OUT=WORK.TMP0TempTableForPlots; BY "Bin BFT01"n; RUN; AXIS1 MAJOR=(NUMBER=5) WIDTH=1; AXIS2 OFFSET=(10 PCT) WIDTH=1; AXIS3 MAJOR=(NUMBER=5) OFFSET=(5 PCT) WIDTH=1; PROC GPLOT DATA=WORK.TMP0TempTableForPlots ; WHERE "Freq BFT01"n IS NOT MISSING AND "Bin BFT01"n IS NOT MISSING;

/* ********* PREDICTED plots ********* */

TITLE4 "Observed 'Freq BFT01' by Predicted 'Freq BFT01'"; SYMBOL1 C=BLUE V=DOT HEIGHT=2PCT INTERPOL=NONE L=1 W=1; LABEL _predicted1 = "Predicted 'Freq BFT01'";

148

WHERE "Freq BFT01"n IS NOT MISSING AND _predicted1 IS NOT MISSING; PLOT "Freq BFT01"n * _predicted1 / VAXIS=AXIS1 VMINOR=0 HAXIS=AXIS3 HMINOR=0 DESCRIPTION = "Observed 'Freq BFT01' by Predicted 'Freq BFT01'" ; RUN;

TITLE4 "Observed 'Freq BFT01' by 'Bin BFT01'"; SYMBOL1 C=BLUE V=DOT HEIGHT=2PCT INTERPOL=NONE L=1 W=1; SYMBOL2 C=BLUE V=NONE INTERPOL=JOIN L=1 W=1; SYMBOL3 C=BLUE V=NONE INTERPOL=JOIN L=1 W=1; SYMBOL4 C=BLUE V=NONE INTERPOL=JOIN L=1 W=1; WHERE "Freq BFT01"n IS NOT MISSING AND "Bin BFT01"n IS NOT MISSING; PLOT ("Freq BFT01"n _predicted1) * "Bin BFT01"n / VAXIS=AXIS1 VMINOR=0 HAXIS=AXIS3 HMINOR=0 DESCRIPTION = "Observed 'Freq BFT01' by 'Bin BFT01'" OVERLAY ; RUN;

SYMBOL; QUIT;

/* ------End of task code. ------*/ RUN; QUIT; %_eg_conditional_dropds(WORK.SORTTempTableSorted, WORK.TMP0TempTableForPlots); TITLE; FOOTNOTE;

149

REFERENCES

Aktaş, N., Tülek, Y., & Gökalp, H. Y. (1997). Determination of differences in free and bound water contents of beef muscle by DSC under various freezing conditions. Journal of Thermal Analysis, 50(4), 617–624. http://doi.org/10.1007/BF01979033 Ali, S., Zhang, W., Rajput, N., Khan, M. A., Li, C., & Zhou, G. (2015). Effect of multiple freeze–thaw cycles on the quality of chicken breast meat. Food Chemistry, 173, 808–814. http://doi.org/10.1016/j.foodchem.2014.09.095 Allen, K. E., & Cornforth, D. P. (2006). Myoglobin oxidation in a model system as affected by nonheme iron and iron chelating agents. Journal of Agricultural and Food Chemistry, 54(26), 10134–10140. http://doi.org/10.1021/jf0623182 Allied Market Research. (2015). Frozen Food Market is Expected to Reach $306 Billion, by 2020. Retrieved October 8, 2015, from http://www.prnewswire.com/news- releases/frozen-food-market-is-expected-to-reach-306-billion-by-2020---allied- market-research-531215901.html American Meat Science Association. (2012). AMSA Meat Color Measurement Guidelines. Champaign, IL. Añón, M. C., & Calvelo, A. (1980). Freezing rate effects on the drip loss of frozen beef. Meat Science, 4(1), 1–14. http://doi.org/10.1016/0309-1740(80)90018-2 AOAC International. (1995). AOAC Official Method 950.46 Moisture in Meat. In AOAC Official Methods of Analysis. Apple, J. K., & Yancey, J. W. S. (2013). Water-Holding Capacity of Meat. In C. Kerth (Ed.), The Science of Meat Quality (pp. 119–145). Oxford, UK: John Wiley & Sons, Inc. http://doi.org/10.1002/9781118530726.ch7

150

Bailey, A. J. (1972). The basis of meat texture. Journal of the Science of Food and Agriculture, 23, 995–1007. http://doi.org/10.1002/jsfa.2740230811 Barbut, S. (1996). Estimates and detection of the PSE problem in young turkey breast meat. Canadian Journal of Animal Science, 76(3), 455–457. http://doi.org/10.4141/cjas96-066 Barsa, C. S., Normand, M. D., & Peleg, M. (2012). On Models of the Temperature Effect on the Rate of Chemical Reactions and Biological Processes in Foods. Food Engineering Reviews, 4(4), 191–202. http://doi.org/10.1007/s12393-012-9056-x Bartlett, L. (1944). A thermodynamic examination of the latent heat of food. Refrigeration Engineering, 47, 377. Bekhit, A., & Faustman, C. (2005). Metmyoglobin reducing activity. Meat Science, 71, 407–439. http://doi.org/10.1016/j.meatsci.2005.04.032 Bertram, H. C., & Andersen, H. J. (2004). Applications of NMR in Meat Science. Annual Reports on NMR Spectroscopy, 53(04), 157–202. http://doi.org/10.1016/S0066- 4103(04)53003-X Bertram, H. C., Karlsson, a H., Rasmussen, M., Pedersen, O. D., Dønstrup, S., & Andersen, H. J. (2001). Origin of multiexponential T2 relaxation in muscle myowater. Journal of Agricultural and Food Chemistry, 49(6), 3092–3100. http://doi.org/10.1021/jf001402t Bertram, H. C., Purslow, P. P., & Andersen, H. J. (2002). Relationship between meat structure, water mobility, and distribution: a low-field nuclear magnetic resonance study. Journal of Agricultural and Food Chemistry, 50(4), 824–9. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11829651 Bertram, H. C., Whittaker, A. K., Andersen, H. J., & Karlsson, A. H. (2004). Visualization of drip channels in meat using NMR microimaging. Meat Science, 68(4), 667–670. http://doi.org/10.1016/j.meatsci.2004.05.005 Bertram, H. C., Whittaker, A. K., Shorthose, W. R., Andersen, H. J., & Karlsson, A. H. (2004). Water characteristics in cooked beef as influenced by ageing and high- pressure treatment - An NMR micro imaging study. Meat Science, 66(2), 301–306. http://doi.org/10.1016/S0309-1740(03)00103-7 Bevilacqua, A. E., & Zaritzky, N. E. (1980). Ice morphology in frozen beef. Journal of Food Technology, 15(6), 589–597. http://doi.org/10.1111/j.1365- 2621.1980.tb00978.x Bevilacqua, A. E., & Zaritzky, N. E. (1982). Ice Recrystallization in Frozen Beef. Journal of Food Science1, 47(2), 1410–1414. Bevilacqua, A., Zaritzky, N., & Calvelo, A. (1979). Histological measurements of ice in 151

frozen beef. Journal of Food Technology, 14, 237–251. Bonny, J. M., & Renou, J. P. (2002). Water diffusion features as indicators of muscle structure ex vivo. Magnetic Resonance Imaging, 20(5), 395–400. http://doi.org/10.1016/S0730-725X(02)00515-5 Bouton, P. E., Harris, P. V, & Shorthose, W. R. (1971). Effect of ultimate pH upon the water-holding capacity and tenderness of mutton. Journal of Food Science, 36(3), 435–439. http://doi.org/10.1111/j.1365-2621.1971.tb06382.x Brown, W. D., & Dolev, A. (1963). Effect of Freezing on Autoxidation of Oxymyoglobin Solutions. Journal of Food Science, 28(2), 211–213. http://doi.org/10.1111/j.1365- 2621.1963.tb00186.x Buttkus, H. (1967). The Reaction of Myosin with Malonaldehyde. Journal of Food Science, 32, 8–10. Buttkus, H. (1970). Accelerated denaturation of myosin frozen solution . J Food Sci, 35(5), 558–562. http://doi.org/10.1111/j.1365-2621.1970.tb04808.x Calkins, C. R., & Hodgen, J. M. (2007). A fresh look at meat flavor. Meat Science, 77(1 SPEC. ISS.), 63–80. http://doi.org/10.1016/j.meatsci.2007.04.016 Calvelo, R. (1981). Recent studies on meat freezing. In R. Lawrie (Ed.), Developments in Meat Science - 2 (pp. 125–158). London: Elsevier Applied Science Publishers. Carpenter, C. E., Cornforth, D. P., & Whittier, D. (2001). Consumer preferences for beef color and packaging did not affect eating satisfaction. Meat Science, 57(4), 359–363. http://doi.org/10.1016/S0309-1740(00)00111-X Chattong, U., Apichartsrangkoon, A., & Bell, A. E. (2007). Effects of hydrocolloid addition and high pressure processing on the rheological properties and microstructure of a commercial ostrich meat product “Yor” (Thai sausage). Meat Science, 76(3), 548–554. http://doi.org/10.1016/j.meatsci.2007.01.012 Chen, L., & Opara, U. L. (2013). Texture measurement approaches in fresh and processed foods - A review. Food Research International, 51(2), 823–835. http://doi.org/10.1016/j.foodres.2013.01.046 Crigler, J. C., & Dawson, L. E. (1968). Cell Disruption in Broiler Breast Muscle Related to Freezing Time. Journal of Food Science1, 33, 248–250. Damez, J. L., & Clerjon, S. (2013). Quantifying and predicting meat and meat products quality attributes using electromagnetic waves: An overview. Meat Science, 95(4), 879–896. http://doi.org/10.1016/j.meatsci.2013.04.037 Daniel, C. R., Cross, A. J., Koebnick, C., & Sinha, R. (2011). Trends in meat consumption in the USA. Public Health Nutrition, 14(4), 575–83. 152

http://doi.org/10.1017/S1368980010002077 Daubert, C., & Foegeding, E. (2010). Rheological Principles for Food Analysis. In S. Nielson (Ed.), Food Analysis2 (4th ed., pp. 541–554). New York, NY: Springer Science & Business Media. Davey, C. L., & Gilbert, K. V. (1969). Studies in Meat Tenderness . 7 . Changes in the Fine Structure of Meat During Aging. Journal of Food Science, 34(150 ml), 69–74. http://doi.org/10.1111/j.1365-2621.1969.tb14364.x DeFreitas, Z., Sebranek, J. G., Olson, D. G., & Carr, J. M. (1997). Carrageenan Effects on Salt-Soluble Meat Proteins in Model Systems. Journal of Food Science, 62(3), 539–543. http://doi.org/10.1111/j.1365-2621.1997.tb04425.x Delgado, A. E., & Sun, D. W. (2002). Desorption isotherms and glass transition temperature for chicken meat. Journal of Food Engineering, 55(1), 1–8. http://doi.org/10.1016/S0260-8774(01)00222-9 den Hertog-Meischke, M. J., van Laack, R. J., & Smulders, F. J. (1997). The water- holding capacity of fresh meat. The Veterinary Quarterly, 19(4), 175–181. http://doi.org/10.1080/01652176.1997.9694767 Di Luca, A., Mullen, A. M., Elia, G., Davey, G., & Hamill, R. M. (2011). Centrifugal drip is an accessible source for protein indicators of pork ageing and water-holding capacity. Meat Science, 88(2), 261–70. http://doi.org/10.1016/j.meatsci.2010.12.033 Dodge, J. W., & Stadelman, W. J. (1959). Studies on Tenderness Evaluation. Poultry Science, 39(1), 184–187. Doerscher, D. R., Briggs, J. L., & Lonergan, S. M. (2004). Effects of pork collagen on thermal and viscoelastic properties of purified porcine myofibrillar protein gels. Meat Science, 66(1), 181–188. http://doi.org/10.1016/S0309-1740(03)00082-2 Empey, W. (1933). Studies on the Refrigeration of Meat. Conditions Determining the Amount of “Drip” from Frozen and Thawed Muscle. Journal of the Society of Chemical Industry, 52(30), 230T–236T. Farouk, M. M., Wieliczko, K. J., & Merts, I. (2004). Ultra-fast freezing and low storage temperatures are not necessary to maintain the functional properties of manufacturing beef. Meat Science, 66(1), 171–179. http://doi.org/10.1016/S0309- 1740(03)00081-0 Fennema, O. (1973). Nature of the Freezing Process. In O. R. Fennema, W. D. Powrie, & E. H. Marth (Eds.), Low-Temperature Preservation of Foods and Living Matter (pp. 150–239). New York: Marcel Dekker, Inc. Fessas, D., & Schiraldi, A. (2001). Water properties in wheat flour dough I: classical thermogravimetry approach. Food Chemistry, 72(2), 237–244. 153

http://doi.org/10.1016/S0308-8146(00)00220-X Findlay, C. J., Parkin, K. L., & Stanley, D. W. (1986). Differential scanning calorimetry can determine kinetics of thermal denaturation of beef muscle protein. Journal of Food Biochemistry, 10(1), 1–15. http://doi.org/10.1111/j.1745-4514.1986.tb00085.x Finn, D. B. B. (1932). Denaturation of Proteins in Muscle Juice by Freezing. Proceedings of the Royal Society of London, 111(772), 396–411. Fletcher, D. L. (2002). Poultry meat quality. World’s Poultry Science Journal, 58(02), 131–145. http://doi.org/http://dx.doi.org/10.1079/WPS20020013 Foucat, L., Taylor, R., Labas, R., & Renou, J. (2001). Characterization of frozen fish by NMR imaging and histology. American Laboratory, 33(16), 38–43. Franks, F. (1995). Protein destabilization at low temperatures. Advances in Protein Chemistry, 46, 105–139. Frozen Food Handling and Merchandising Alliance. (2009). Frozen Food: Handling and Merchandising. McLean, Virginia. Retrieved from http://www.affi.org/assets/resources/public/frozen-food-handling-and- merchandising-2009-f.pdf Funami, T., Yada, H., & Nakao, Y. (1998). Thermal and rheological properties of curdlan gel in minced pork gel. Food Hydrocolloids, 12, 55–64. http://doi.org/10.1016/S0268-005X(98)00045-9 Gotoh, T., & Shikama, K. (1974). Autoxidation of Native Oxymyoglobin from Bovine Heart Muscle. Archives of Biochemistry and Biophysics, 163, 476–481. Gray, J. I., Pearson, A. M., & Monahan, F. J. (1994). Flavor and aroma problems and their measurement in meat, poultry and fish products. In A. M. Pearson & T. R. Dutson (Eds.), Quality Attributes and their Measurement in Meat, Poultry and Fish Products (Vol. 3, pp. 250–288). Boston, MA: Springer US. http://doi.org/10.1007/978-1-4615-2167-9_10 Guiheneuf, T. M., Parker, A. D., Tessier, J. J., & Hall, L. D. (1997). Authentication of the effect of freezing/thawing of pork by quantitative magnetic resonance imaging. Magnetic Resonance in Chemistry, 35(13), S112–S118. http://doi.org/10.1002/(SICI)1097-458X(199712)35:133.0.CO;2-R Hall, L. D., Evans, S. D., & Nott, K. P. (1998). Measurement of textural changes of food by MRI relaxometry. Magnetic Resonance Imaging, 16(98), 485–492. http://doi.org/10.1016/S0730-725X(98)00116-7 Hamm, R. (1960). Biochemistry of Meat Hydration. Advances in Food Research, 10, 355–463. http://doi.org/10.1556/AAlim.2015.0002 154

Hansen, E., Juncher, D., Henckel, P., Karlsson, A., Bertelsen, G., & Skibsted, L. H. (2004). Oxidative stability of chilled pork chops following long term freeze storage. Meat Science, 68(3), 479–484. http://doi.org/10.1016/j.meatsci.2004.05.002 Hay, J., Currie, R., Wolfe, F., & Sanders, E. (1972). Effect of Postmortem Aging on Chicken Muscle Fibrils. Journal of Food Science, 38, 981–986. Heldman, D. (1974). Predicting the relationship between unfrozen water fraction and temperature during food freezing using freezing point depression. Trans. ASAE, 63– 66. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Predicting+the+R elationship+Between+Unfrozen+Water+Fraction+and+Temperature+During+Food+ Freezing+Using+Freezing+Point+Depression#0 Hessler, J. P. (1997). The use of Monte Carlo simulations to evaluate kinetic data and analytic approximations. International Journal of Chemical Kinetics, 29(11), 803– 817. http://doi.org/10.1002/(SICI)1097-4601(1997)29:11<803::AID- KIN1>3.0.CO;2-Q Honikel, K. O. (1998). Reference methods for the assessment of physical characteristics of meat. Meat Science, 49, 447–457. Huff-Lonergan, E., & Lonergan, S. M. (2005). Mechanisms of water-holding capacity of meat: The role of postmortem biochemical and structural changes. Meat Science, 71(1), 194–204. http://doi.org/10.1016/j.meatsci.2005.04.022 Huffman, K., Zapata, I., Reddish, J. M., Lilburn, M. S., & Wick, M. (2012). Feed restriction delays developmental fast skeletal muscle myosin heavy chain isoforms in turkey poults selected for differential growth. Poultry Science, 91(12), 3178–83. http://doi.org/10.3382/ps.2012-02367 Ingram, M., & Dainty, R. H. (1971). Changes caused by microbes in spoilage of meats. The Journal of Applied Bacteriology, 34(1), 21–39. http://doi.org/10.1111/j.1365- 2672.1971.tb02266.x Ishioroshi, M., Samejima, K., & Yasui, T. (1979). Heat-induced gelation of myosin: factors of pH and salt concentrations. Journal of Food Science, 44(5), 1280–1284. http://doi.org/10.1111/j.1365-2621.1979.tb06419.x James, S. J., & James, C. (2002). Microbiology of Refrigerated Meat. In Meat Refrigeration (pp. 3–20). Boca Raton, FL: CRC Press. James, S., Nair, C., & Bailey, C. (1984). The effect of the rate of freezing and thawing on the drip loss from frozen beef. In P. Zeuthen, J. Cheftel, C. Eriksson, M. Jul, H. Leniger, P. Linko, … G. Vos (Eds.), Thermal Processing and Quality of Foods (pp. 807–812). Barking, Essex: Elsevier Applied Science Publishers.

155

James, S. S., & James, C. (2012). Quality and Safety of Frozen Meat and Meat Products. In D.-W. Sun (Ed.), Handbook of Frozen Food Processing and Packaging (2nd ed., Vol. 18, pp. 303–323). London: CRC Press. Jeong, J.-Y., Kim, G.-D., Yang, H.-S., & Joo, S.-T. (2011). Effect of freeze–thaw cycles on physicochemical properties and color stability of beef semimembranosus muscle. Food Research International, 44(10), 3222–3228. http://doi.org/10.1016/j.foodres.2011.08.023 Joo, S. ., Kauffman, R. ., Kim, B. ., & Park, G. . (1999). The relationship of sarcoplasmic and myofibrillar protein solubility to colour and water-holding capacity in porcine longissimus muscle. Meat Science, 52(3), 291–297. http://doi.org/10.1016/S0309- 1740(99)00005-4 Jul, M. (1982). The intracacies of the freezer chain. International Journal of Refrigeration, 5(4), 226–230. http://doi.org/10.1016/0140-7007(82)90025-1 Jul, M. (1984). The Quality of Frozen Food. London: Academic Press, Inc. Kerr, W. L. (2008). Magnetic Resonance Studies of Food Freezing. In G. A. Webb (Ed.), Modern Magnetic Resonance (pp. 1869–1878). Springer. Khan, A., Van Den Berg, L., & Lentz, C. P. (1962). Effects of Frozen Storage on Chicken Muscle Proteins. Journal of Food Science, 28(4), 425–430. Kiessling, K. H. (1977). Muscle structure and function in the goose, quail, pheasant, guinea hen, and chicken. Comparative Biochemistry and Physiology -- Part B: Biochemistry and, 57(4), 287–292. http://doi.org/10.1016/0305-0491(77)90055-4 Kijowski, J. M., & Mast, M. G. (1988). Effect of Sodium Chloride and Phosphates on the Thermal Properties of Chicken Meat Proteins. Journal of Food Science, 53(2), 367– 370. http://doi.org/10.1111/j.1365-2621.1988.tb07707.x Lai, D.-J. (1984). Quantification of the Freeze-Concentration Effect on the Quality Degradation Rates During Frozen Food Storage. Michigan State University. Lawrie, R., & Ledward, D. (Eds.). (2006a). The Eating Quality of Meat. In Lawrie’s meat science (7th ed., pp. 279–341). Boca Raton, FL: CRC Press. Lawrie, R., & Ledward, D. A. (2006b). Introduction. In R. Lawrie & D. A. Ledward (Eds.), Lawrie’s meat science (7th ed., pp. 1–14). Boca Raton, FL: CRC Press. Lee, Y. S., Owens, C. M., & Meullenet, J. F. (2008). The Meullenet-Owens Razor Shear (MORS) for redicting poultry meat tenderness: Its applications and optimization. Journal of Texture Studies, 39(6), 655–672. http://doi.org/10.1111/j.1745- 4603.2008.00165.x LeMeste, M., Champion, D., Roudaut, G., Blond, G., & Simatos, D. (2002). Glass 156

Transition and Food Technology: A Critical Appraisal. Journal of Food Science, 67(7), 2444–2458. Leygonie, C., Britz, T. J., & Hoffman, L. C. (2012). Impact of freezing and thawing on the quality of meat: review. Meat Science, 91(2), 93–8. http://doi.org/10.1016/j.meatsci.2012.01.013 Li, C. T., & Wick, M. (2001). Improvement of the physicochemical properties of pale soft and exudative (PSE) pork meat products with an extract from mechanically deboned turkey meat (MDTM). Meat Science, 58(2), 189–195. http://doi.org/10.1016/S0309-1740(00)00151-0 Li, K., Heaton, E., & Marion, J. (1969). Freezing Chicken Thighs by Liquid Nitrogen and Sharp Freezing Process. Food Technology, 23(2), 107–109. Lindeløv, F. (1978). Reactions in frozen foods; the reactions of myosin and single amino acids with some aldehydes. International Journal of Refrigeration. http://doi.org/10.1016/0140-7007(78)90014-2 Listrat, A., Lebret, B., Louveau, I., Astruc, T., Bonnet, M., Lefaucheur, L., … Bugeon, J. (2016). How muscle structure and composition influence meat and flesh quality. Scientific World Journal, 2016. http://doi.org/10.1155/2016/3182746 Lodi, A., Abduljalil, A. M., & Vodovotz, Y. (2007). Characterization of water distribution in bread during storage using magnetic resonance imaging. Magnetic Resonance Imaging, 25(10), 1449–58. http://doi.org/10.1016/j.mri.2007.04.003 Lodi, A., Tiziani, S., & Vodovotz, Y. (2007). Molecular changes in soy and wheat breads during storage as probed by nuclear magnetic resonance (NMR). Journal of Agricultural and Food Chemistry, 55(14), 5850–7. http://doi.org/10.1021/jf070636a Love, R. (1957). The Expressible Fluid of Fish Fillets. V. - Cell Damage in Fillets Frozen from One Side: the General Picture. Journal of the Science of Food and Agriculture, 8, 238–242. Macfarlane, J. J., Schmidt, G. R., & Turner, R. H. (1977). Binding of meat pieces: a comparison of myosin, actomyosin and sarcoplasmic proteins as binding agents. Journal Of Food Science, 42(6), 1603–1605. http://doi.org/doi:10.1111/j.1365- 2621.1977.tb08437.x Mancini, R. A., & Hunt, M. C. (2005). Current research in meat color. Meat Science, 71(1), 100–121. http://doi.org/10.1016/j.meatsci.2005.03.003 Miller, A. J., Ackerman, S. A., & Palumbo, S. A. (1980). Effects of Frozen Storage on Functionality of Meat for Processing. Journal of Food Science, 45, 1466–1471. Moran, T. (1932). Rapid Freezing. Critical Rate of Cooling. Journal of the Society of Chemical Industry, 51, 16T–20T. 157

National Frozen & Refrigerated Foods Association. (2009). History of Frozen Foods is Long and Varied. Retrieved from https://www.nfraweb.org/resources/articles/details.aspx?ArticleId=18 Nowak, D. (2011). Enzymes in Tenderization of Meat - The System of Calpains and Other Systems - a Review. Pol. J. Food Nutr. Sei, 61(4), 251–256. Retrieved from http://joumal.pan.olsztyn.pl Offer, G., & Trinick, J. (1983). On the mechanism of water holding in meat: The swelling and shrinking of myofibrils. Meat Science, 8(4), 245–281. http://doi.org/10.1016/0309-1740(83)90013-X Olson, D., Parrish, F., & Stromer, M. (1976). Myofibril Fragmentation and Shear Resistance of Three Bovine Muscles During Postmortem Storage. Journal of Food Science, 41(5), 1036–1041. http://doi.org/10.1111/j.1365-2621.1976.tb14384.x OpenStax. (2014). Chapter 1. Introduction. Retrieved October 17, 2016, from http://cnx.org/contents/[email protected]:zMTtFGyH@4/Introduction Owens, C. M., Hirschler, E. M., McKee, S., Martinez-Dawson, R., & Sams, A. R. (2000). The characterization and incidence of pale, soft, and exudative broiler meat in a commercial processing plant. Poultry Science, 79(4), 553–558. http://doi.org/10.1093/ps/81.4.579 Peleg, M., Engel, R., Gonzalez-Martinez, C., & Corradini, M. G. (2002). Non-Arrhenius and non-WLF kinetics in food systems. Journal of the Science of Food and Agriculture, 82(12), 1346–1355. http://doi.org/10.1002/jsfa.1175 Peleg, M., Normand, M. D., & Corradini, M. G. (2012). The Arrhenius equation revisited. Critical Reviews in Food Science and Nutrition, 52(9), 830–51. http://doi.org/10.1080/10408398.2012.667460 Penny, I. F. (1975). Use of a centrifuging method to measure the drip of pork Longissimus dorsi slices before and after freezing and thawing. Journal Of The Science Of Food And Agriculture, 26, 1593–1602. Perez-Chabela, M., Mateo-Oyagüe, J., Pérez-Chabela, M. L., Mateo-Oyagüe, J., Hui, Y. H., Cornillon, P., … Mateo-Oyague, J. (2004). Frozen Meat: Quality and Shelf Life. In Y. H. Hui (Ed.), Handbook of Food Science, Technology, and Engineering (Vol. 3, pp. 201–214). Boca Raton, FL: CRC/Taylor & Francis. http://doi.org/10.1556/AAlim.2015.0002 Petracci, M., Bianchi, M., Mudalal, S., & Cavani, C. (2013). Functional ingredients for poultry meat products. Trends in Food Science and Technology, 33(1), 27–39. http://doi.org/10.1016/j.tifs.2013.06.004 Petracci, M., Sirri, F., Mazzoni, M., & Meluzzi, a. (2013). Comparison of breast muscle

158

traits and meat quality characteristics in 2 commercial chicken hybrids. Poultry Science, 92(9), 2438–47. http://doi.org/10.3382/ps.2013-03087 Petrović, L., Grujić, R., & Petrović, M. (1993). Definition of the optimal freezing rate-2. Investigation of the physico-chemical properties of beef M. longissimus dorsi frozen at different freezing rates. Meat Science, 33(3), 319–31. http://doi.org/10.1016/0309- 1740(93)90004-2 Petzold, G., & Aguilera, J. M. (2009). Ice Morphology: Fundamentals and Technological Applications in Foods. Food Biophysics, 4(4), 378–396. http://doi.org/10.1007/s11483-009-9136-5 Pham, Q. T., & Mawson, R. F. (1997). Moisture migration and ice recrystallization in frozen foods. Quality in Frozen Food, 67–91. http://doi.org/10.1007/978-1-4615- 5975-7_5 Privalov, P. L., & Makhatadze, G. I. (1993). Contribution of hydration to protein folding thermodynamics. II. The entropy and Gibbs energy of hydration. Journal of Molecular Biology. http://doi.org/10.1006/jmbi.1993.1417 Puolanne, E., & Halonen, M. (2010). Theoretical aspects of water-holding in meat. Meat Science, 86(1), 151–165. http://doi.org/10.1016/j.meatsci.2010.04.038 Ramsbottom, J. M., & Koonz, C. H. (1939). Freezing temperature as related to drip of frozen-defrosted beef. Food Research, 5, 425–431. Rasmussen, A., & Andersson, M. (1996). New method for determination of drip loss in pork muscles. In Hildrum & Risvik (Eds.), Proceedings of the 42nd International Congress of Meat Science and Technology (pp. 286–287). Lillehammer, Norway. Reid, D. S. (1997). Overview of Physical/Chemical Aspects of Freezing. In M. C. Erickson & Y.-C. Hung (Eds.), Quality in Frozen Food (pp. 10–28). New York: Chapman and Hall. Renerre, M., Anton, M., & Gatellier, P. (1992). Autoxidation of purified myoglobin from two bovine muscles. Meat Science, 32(3), 331–342. http://doi.org/10.1016/0309- 1740(92)90096-M Renou, J. P., Foucat, L., & Bonny, J. M. (2003). Magnetic resonance imaging studies of water interactions in meat. Food Chemistry, 82, 35–39. http://doi.org/10.1016/S0308-8146(02)00582-4 ReportLink. (2013). Frozen Food Industry: Market Research Reports, Statistics and Analysis. Retrieved from http://www.reportlinker.com/ci02043/Frozen-Food.html Reuhs, B. L., & Simsek, S. (2010). Nuclear Magnetic Resonance. In S. S. Nielson (Ed.), Food Analysis (4th ed., pp. 443–456). New York: Springer.

159

Rosenberg, I. (2005). Electrophoretic Techniques. In Protein Analysis and Purification: Benchtop Techniques (pp. 63–117). Boston, MA: Birkh{ä}user Boston. http://doi.org/10.1007/0-8176-4412-1_4 Sacks, B., Casey, N. H., Boshof, E., & Zyl, H. Van. (1993). Influence of Freezing Method on Thaw Drip and Protein Loss of Low-Voltage Electrically Stimulated and Non-Stimulated Sheeps ’ Muscle. Meat Science, 34, 235–243. http://doi.org/10.1016/0309-1740(93)90030-L Samejima, K., Ishioroshi, M., & Yasui, T. (1981). Relative roles of head and tail portions of the molecule in heat-induced gelation of myosin. Journal Of Food Science, 46(5), 1412–1418. http://doi.org/10.1111/j.1365-2621.1981.tb04187.x Sano, T., Noguchi, S., & Tsuchiya, T. (1988). Dynamic viscoelastic behavior of natural actomyosin and myosin during thermal gelation. Journal of Food Scinece, 53, 924. http://doi.org/10.1111/j.1365-2621.1988.tb08987.x Sayre, R. N. (1970). Chicken Myofibril Fragmentation in Relation to Factors Influencing Tenderness. Journal of Food Science, 35, 7–10. Scheffler, T.L., J.M. Scheffler, S.C. Kasten, A.A. Sosnicki and D.E. Gerrard. 2013. High glycolytic potential does not predict low ultimate pH in pork. Meat Science, 95: 85- 91 Schnepf, M., & Parsegian, V. A. (1992). Protein-water interactions. In B. Hudson (Ed.), Biochemistry of Food Proteins (Vol. 215, pp. 1–33). Springer Science & Business Media. http://doi.org/10.1016/S0074-7696(02)15003-0 Shaarani, S. M., Nott, K. P., & Hall, L. D. (2006). Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat. Meat Science, 72(3), 398–403. http://doi.org/10.1016/j.meatsci.2005.07.017 Shackelford, S. D., Wheeler, T. L., & Koohmaraie, M. (1995). Relationship between shear force and trained sensory panel tenderness ratings of 10 major muscles from Bos indicus and Bos taurus cattle. Journal of Animal Science, 73(11), 3333–3340. http://doi.org//1995.73113333x Shahidi, F., Rubin, L. J., & Wood, D. F. (1987). Control of lipid oxidation in cooked meats by combinations of antioxidants and chelators. Food Chemistry, 23(2), 151– 157. http://doi.org/10.1016/0308-8146(87)90009-4 Shen, Q., & Du, M. (2016). Conversion of Muscle to Meat. In W. Przybylski & D. Hopkins (Eds.), Meat Quality: Genetic and Environmental Factors (pp. 81–100). Boca Raton, FL: CRC Press. Simmons, A. L., Smith, K. B., & Vodovotz, Y. (2012). Soy ingredients stabilize bread

160

dough during frozen storage. Journal of Cereal Science, 56(2), 232–238. http://doi.org/10.1016/j.jcs.2012.05.007 Slade, L., & Levine, H. (1988). Non-Equilibrium Behavior of Small Carbohydrate-Water Systems. Pure and Applied Chemistry, 60(12), 1841–1864. Slade, L., & Levine, H. (1991). Beyond water activity: recent advances based on an alternative approach to the assessment of food quality and safety. Critical Reviews in Food Science and Nutrition, 30(2-3), 115–360. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1854434 Smith, D. P., Lyon, C.E., Fletcher, D. L. (1988). Comparison of the Allo-Kramer shear and Texture Profile Method of Broiler Breast meat texture Analysis. Poult. Sci.1, 67(1981), 1549–1556. Smith, G., Blek, K., Sofos, J., Tatum, J., & Williams, S. (2000). Economic Implications of Improved Color Stability in Beef. In E. Decker, C. Faustman, & C. Lopez-Bote (Eds.), Antioxidants in Muscle Foods: Nutritional Strategies to Improve Quality (pp. 397–426). New York: Wiley-Interscience. Sofos, J. N. (2008). Challenges to meat safety in the 21st century. Meat Science, 78(1-2), 3–13. http://doi.org/10.1016/j.meatsci.2007.07.027 Sosnicki, A. A., Greaser, M. L., Pietrzak, M., Pospiech, E., & Sante, V. (1998). PSE-like Syndrome in Breast Muscle of Domestic Turkeys: A Review. Journal of Muscle Foods, 9(1), 13–23. http://doi.org/10.1111/j.1745-4573.1998.tb00640.x Stanley, D. W. (1983). Relation of Structure to Physical Properties of Animal Material. In M. Peleg & E. Bagley (Eds.), Physical Properties of Foods (pp. 157–206). Westport, CT: AVI Publishing Company, Inc. Szczesniak, A. S. (1998). Effect of Storage on Texture. In I. Taub & R. P. Singh (Eds.), Food Storage Stability (pp. 191–243). Boca Raton, FL: CRC Press. Szczesniak, A. S. (2002). Texture is a sensory property. Food Quality and Preference, 13(4), 215–225. http://doi.org/10.1016/S0950-3293(01)00039-8 Tamilmani, P., & Pandey, M. C. (2016). Thermal analysis of meat and meat products. Journal of Thermal Analysis and Calorimetry, 123(3), 1899–1917. http://doi.org/10.1007/s10973-015-4696-8 Tang, J., Faustman, C., & Hoagland, T. A. (2004). Krzywicki Revisited: Equations for Spectrophotometric Determination of Myoglobin Redox Forms in Aqueous Meat Extracts. Journal of Food Science, 69(9), 717–720. Thomas, L., & Schmidt, S. (2010). Thermal Analysis. In S. Nielson (Ed.), Food Analysis (4th ed., pp. 555–571). New York, NY: Springer Science & Business Media.

161

Tornberg, E. (2005). Effects of heat on meat proteins - Implications on structure and quality of meat products. Meat Science, 70(3 SPEC. ISS.), 493–508. http://doi.org/10.1016/j.meatsci.2004.11.021 Tornberg, E., Andersson, K., & Josell, Å. (1997). The rheological properties of whole and minced meat during cooking as related to sensory and structural characteristics. In Proceedings of the 1st international symposium on food rheology and structure (pp. 16–20/3). Zurich. Trout, G. R. (1988). Techniques for measuring water-binding capacity in muscle foods-A review of methodology. Meat Science, 23(4), 235–252. http://doi.org/10.1016/0309- 1740(88)90009-5 Troy, D. J., & Kerry, J. P. (2010). Consumer perception and the role of science in the meat industry. Meat Science, 86(1), 214–226. http://doi.org/10.1016/j.meatsci.2010.05.009 United States Department of Agriculture (USDA). (1999). Appendix B: Compliance Guidelines for Cooling Heat-Treated Meat and Poultry Products (Stabilization). Retrieved January 1, 2016, from http://www.fsis.usda.gov/OPPDE/rdad/FRPubs/95- 033F/95-033F_Appendix B.htm Updike, M. S., Zerby, H. N., Sawdy, J. C., Lilburn, M. S., Kaletunc, G., & Wick, M. P. (2005). Turkey breast meat functionality differences among turkeys selected for body weight and/or breast yield. Meat Science, 71(4), 706–712. http://doi.org/10.1016/j.meatsci.2005.05.014 Updike, M. S., Zerby, H., Utrata, K. L., Lilburn, M., Kaletunc, G., & Wick, M. (2006). Proteins associated with thermally induced gelation of turkey breast meat. Journal of Food Science, 71(9). http://doi.org/10.1111/j.1750-3841.2006.00184.x Van Laack, R. L., Liu, C. H., Smith, M. O., & Loveday, H. D. (2000). Characteristics of pale, soft, exudative broiler breast meat. Poultry Science, 79, 1057–1061. http://doi.org/10.1093/ps/79.7.1057 Veiseth, E., Shackelford, S. D., Wheeler, T. L., & Koohmaraie, M. (2001). Technical Note: Comparison of myofibril fragmentation index from fresh and frozen pork and lamb longissimus. Journal of Animal Science, 79(4), 904–906. http://doi.org/79:904–906 Venturi, L. (2007). NMR Study of Meat as Related to its Structural Organisation. Universita’ di Bologna. Venturi, L., Rocculi, P., Cavani, C., Placucci, G., Dalla Rosa, M., & Cremonini, M. a. (2007). Water absorption of freeze-dried meat at different water activities: A multianalytical approach using sorption isotherm, differential scanning calorimetry, and nuclear magnetic resonance. Journal of Agricultural and Food Chemistry, 162

55(26), 10572–10578. http://doi.org/10.1021/jf072874b Vodovotz, Y., Vittadini, E., & Sachleben, J. R. (2002). Use of 1H cross-relaxation nuclear magnetic resonance spectroscopy to probe the changes in bread and its components during aging. Carbohydrate Research, 337(2), 147–153. http://doi.org/10.1016/S0008-6215(01)00293-2 Wagner, J. R., & Añon, M. C. (1985). Effect of freezing rate on the denaturation of myofibrillar proteins. Journal of Food Technology, 20(6), 735–744. http://doi.org/10.1111/j.1365-2621.1985.tb01971.x Wang, W., & Roberts, C. J. (2013). Non-Arrhenius Protein Aggregation. The AAPS Journal, 15(3), 840–851. http://doi.org/10.1208/s12248-013-9485-3 Weber, K., & Osborn, M. (1969). The Reliability of Molecular Weight Determinations by Dodecyl The Reliability of Molecular Weight Determinations by Dodecyl Sulfate- Polyacrylamide Gel Electrophoresis. Journal of Biological Chemistry, 244, 4406– 4412. Westphalen, A. D., Briggs, J. L., & Lonergan, S. M. (2005). Influence of pH on rheological properties of porcine myofibrillar protein during heat induced gelation. Meat Science, 70(2), 293–299. http://doi.org/10.1016/j.meatsci.2005.01.015 Wu, J. Y., Bryant, R. G., & Eads, T. M. (1992). Detection of solid-like components in starch using cross-relaxation and Fourier transform wide-line proton NMR method. Journal of Agricultural and Food Chemistry, 40(3), 449–455. Xia, X., Kong, B., Liu, Q., & Liu, J. (2009). Physicochemical change and protein oxidation in porcine longissimus dorsi as influenced by different freeze-thaw cycles. Meat Science, 83(2), 239–245. http://doi.org/10.1016/j.meatsci.2009.05.003 Xiong, Y. L. (1997). Protein denaturation and functionality losses. Quality in Frozen Food, 111–139. http://doi.org/10.1007/978-1-4615-5975-7 Yu, X. L., Li, X. B., Zhao, L., Xu, X. L., Ma, H. J., Zhou, G. H., & Boles, J. A. (2010). Effects of Different Freezing Rates and Thawing Rates on the Manufacturing Properties and Structure of Pork. Journal of Muscle Foods, 21(2010), 177–196. http://doi.org/10.1111/j.1745-4573.2009.00175.x Zachariah, N. Y., & Satterlee, L. D. (1973). Effect of light, pH, and buffer strength on the autoxidation of porcine, ovine and bovine myoglobins at freezing temperatures. Journal of Food Science, 38, 418–420. Zanoni, S., & Zavanella, L. (2012). Chilled or frozen? Decision strategies for sustainable food supply chains. International Journal of Production Economics, 140(2), 731– 736. http://doi.org/10.1016/j.ijpe.2011.04.028 Zaritzky, N. E., & Martino, M. N. (1988). Ice Crystal Size Modifications during Frozen 163

Beef Storage. Journal of Food Science, 53(6), 1631–1637. http://doi.org/10.1111/j.1365-2621.1988.tb07802.x

164