Influence of Storage Temperature on Changes in Frozen Meat Quality

Home , Frozen food

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Jeffrey Caminiti

Graduate Program in Food Science and Technology

The Ohio State University

2018

Thesis Committee

Dennis Heldman, Advisor

Macdonald Wick, Advisor

Christopher Simons

Copyrighted by

Jeffrey T. Caminiti

2018

Abstract

Food is often frozen to prolong shelf-life by maintaining safety and high quality.

Since frozen food storage is energy intensive, careful evaluation of the influence of storage temperature on shelf-life is needed. Although the shelf-life of frozen meat at -

18°C may be desirable, the influence of slightly higher storage temperatures on shelf-life have not been thoroughly investigated. Through the understanding of quality degradation reactions and their dependence on temperature, an argument may be made to encourage storage at a more sustainable temperature. The objective was to evaluate the effect of storage temperature on frozen chicken and ground beef quality attributes to identify improved energy efficiencies during storage.

Whole muscle chicken breasts (pectoralis major) were frozen to -20°C [-4°F] then stored at -10°C [14°F], -15°C [5°F], or -20°C for one year. In a completely randomized design monthly quality testing was conducted on three replicates thawed overnight to

4°C. Quality analysis consisted of % drip loss measurements, water holding capacity

(WHC), moisture content (WBMC), lipid oxidation by 2-thiobarbituric acid assay

(TBARS), color, and cooked texture analysis by Blunt Meullenet-Owens Razor Shear

(BMORS). Differences in temperature conditions across time were observed in % drip loss, WHC, L*a*b*, and BMORS (p<0.05). The creation of a shelf-life prediction model based on % drip loss results can be used to assess risk to processors considering ii increasing storage temperatures. This study has shown the potential energy savings may be accomplished without dramatic losses in quality by increasing storage temperatures modestly.

In a completely randomized study 297 ground beef experimental units consisting of 90 patties were packaged in one of three ways then frozen to -22°C . Packaging included: plastic overwrap; a high oxygen permeability package, OTR <0.1 cc/100 in2/day; and a low oxygen permeability package, OTR <0.05cc/100in2/day. The units were later distributed to one nine chest freezers stored at -10°C [14°F], -15°C [5°F], or -

20°C for one year. Color in the L*a*b* color space and lipid oxidation via TBARS were collected monthly. Prior to analysis, meat was thawed at 4°C for 24 hours.

Shelf-life was not improved from improved packaging. Statistically the barriers were used as additional replications for a robust analysis of temperature. The change in redness (a*) over time followed second order rate kinetics. Arrhenius activation energy for a* change was calculated to be 122.3 kj/mol. TBARS data was fit to a modified

Gompertz model (R2=0.91). Predicted maximum TBARS was dependent on temperature and greatest under -10°C, followed by -15°C, and -20°. The state and availability of the unfrozen water may play a role in maximum TBARS observation. Similar rates in the colder temperatures provide an opportunity to reevaluate storage conditions for high-fat products

Observations made on whole muscle chicken and ground beef indicate potential energy savings during frozen storage. The models produced show the measurable reduction in quality would be only minor due to small increases in storage temperature.

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Due to apparent asymptotes and non-Arrhenius rate constants future work must involve a wide range of storage temperatures for the development of empirical models as a function of temperature

Acknowledgments

A special thanks to my parents and brothers, your continued support throughout my education has been everything. My years of school have been full of unexpected challenges; I would not have made it this far without your love and guidance.

Thank you to The Ohio State University department of Food, Science, and

Technology as well as the department of Animal Sciences. The education I have received through my time at this university has been incredibly valuable. The facilities and opportunities available are greatly appreciated.

I want to thank my committee: Dr. Dennis R. Heldman, Dr. Macdonald Wick, Dr.

Christopher Simons. Your support and expertise before and during my master’s work have been invaluable. You have all helped me hone my interests in scientific pursuits.

I am especially grateful for the generosity of Dale A. Seiberling to the Food

Engineering Research Laboratory at The Ohio State University for providing a home to my research project. A special thanks is owed to David M. Phinney and John Frelka for sharing knowledge in and out of the laboratory that became crucial to my success.

Without David and John, the difficult timeline involved in these 12-month studies would not have been possible. Furthermore, Dr. John Frelka’s successful proposal and the Ohio

Agricultural Research and Development Center SEEDS program have made this research a

v reality. A final thanks to the livestock and the generous industry suppliers of the copious amount of meat samples used in these studies.

Vita

2011...... Elder High School

2014, 2015...... Internship, Perfetti van Melle

2016...... B.S. Food Science, Ohio State University

2017 to present ...... Graduate Research Associate, Department

of Food, Science, & Technology, The Ohio

State University

Fields of Study

Major Field: Food, Science, & Technology

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Table of Contents

Abstract ...... ii Acknowledgments...... v Vita ...... vii List of Tables ...... xii List of Figures ...... xiii Chapter 1. Introduction ...... 1 1.1 Objectives: ...... 3 1.2 References: ...... 4 Chapter 2. Review of Literature...... 6 2.1 Phenomena of sub-freezing water in food ...... 6 2.1.1 Unfrozen water content in food ...... 6 2.1.2 Recrystallization in frozen foods ...... 7 2.1.3 Glass transitions state ...... 7 2.2 Water holding capacity in muscle foods: measurements and deterioration ...... 9 2.2.1 Water holding in meat processing (yield and freezing rate) ...... 9 2.2.2 Mechanisms in water holding...... 11 2.2.2 Other methods of measuring water holding ...... 12 2.3 Color loss in meats: sensory considerations, measurements, mechanisms, & kinetics ...... 12 2.3.1 Color perception and consumer acceptance ...... 12 2.3.2 Instrumental measurement of color ...... 13 2.3.3 Myoglobin oxidation kinetics effects on meat color ...... 14 2.4 Texture analysis of chicken breasts ...... 16 2.5 Lipid oxidation: mechanisms, history, and detection methodologies ...... 19 2.5.1 Lipid oxidation Introduction & reaction progression ...... 19 2.5.2 Past reviews of lipid oxidation in meat research ...... 22 2.5.3 Detection of quantification of lipid oxidation in meat ...... 23 2.5.4 TBARS history and distillation method development ...... 24 2.5.5 TBARS extraction method development ...... 27 viii

2.5.6 Applications of TBRS in meat quality research ...... 28 2.6 Extended shelf-life studies of meat: ...... 29 2.7 Magnetic Resonance Imaging (MRI) as a tool for meat analysis ...... 33 2.8 References: ...... 35 Chapter 3: The effect of variable frozen storage temperatures on chicken quality and water holding attributes...... 47 Abstract ...... 47 3.1 Introduction: ...... 48 3.2 Materials and Methods:...... 50 3.2.1. Sample acquisition and freezing ...... 50 3.2.2 Sample storage and analysis: ...... 50 3.2.3 Drip loss ...... 51 3.2.4 Cooking and BMORS ...... 51 3.2.5 Color: ...... 52 3.2.6 Water holding capacity ...... 52 3.2.7. 2-Thiobarbituric acid reactive substances: ...... 53 3.2.8 Moisture content ...... 53 3.2.9 Data analysis: ...... 54 3.3 Results & discussion: ...... 54 3.3.1 Evaluating the effects of time and temperature on quality parameters: ...... 54 3.3.2 Drip loss shelf-life prediction model and analysis:...... 58 3.4 Discussion: ...... 60 3.4.1 The influence of product and handling on results...... 60 3.4.2: The role of water in frozen muscle quality ...... 62 3.4.2: Quality loss in a muscle system ...... 64 3.5 Conclusions & Recommendations ...... 66 3.6 References ...... 67 3.7 Tables and Figures ...... 73 Chapter 4: Effect of storage time, temperature and package on lipid oxidation and color of frozen ground beef patties ...... 80 Abstract ...... 80 4.1 Introduction: ...... 81 4.2. Materials and Methods:...... 83 ix

4.2.1. Sample packaging ...... 83 4.2.2 Product Freezing ...... 83 4.2.3 Product Storage ...... 84 4.2.4 Sample Preparation ...... 84 4.2.4. 2-Thiobarbituric acid reactive substances (TBARS): ...... 85 4.2.5 Color: ...... 85 4.2.6 Statistical analysis: ...... 86 4.3 Results and discussion ...... 86 4.3.1 Influences of packaging on quality attributes ...... 86 4.3.2 ANOVA effects analysis...... 90 4.3.3 Influence of temperature on changes in lipid oxidation and color during storage ...... 92 4.3.4 TBARS modeling: theoretical consideration ...... 93 4.3.5 TBARS modeling: selection and analysis...... 95 4.3.6 TBARS modeling: time and temperature prediction ...... 97 4.3.7 Redness (a*) modeling: regression and analysis ...... 98 4.3.8 Redness (a*) modeling: time and temperature prediction ...... 100 4.4 Conclusions: ...... 101 4.5 References: ...... 102 4.5 Tables and Figures ...... 107 Chapter 5: Conclusions ...... 120 Bibliography ...... 122 Appendix A. Methodology flow chart, moisture balance, and full results; additions to Chapter .3: The effect of variable frozen storage temperatures on chicken quality and water holding attributes...... 139 A.1 Introduction: ...... 139 A.2 Methodology flow chart and moisture balance: ...... 140 A.3 Drip Loss Mass Balance: ...... 140 A.4 Additional Methods: ...... 141 A.2.1 Cook loss: ...... 141 A.2.2 Dynamic Rheological properties: ...... 141 A.2.3 SDS PAGE ...... 142 A.2.4 Magnetic Resonance imaging: ...... 143

A.3 Brief discussion of figures: ...... 143 A.4 Additional tables & figures: ...... 146 A.4.1Addition correlation tables from Chapter 3 ...... 156 Appendix B. Moisture analysis of ground beef additional result from Ch. 4 : Effect of storage time, temperature and package on lipid oxidation and color of frozen ground beef patties ...... 159 B.1Beef Moisture content method: ...... 159 B.2 Wet Basis Moisture Content discussion ...... 159 Appendix B Tables and Figures ...... 161 Appendix C: Procedure for Non-isothermal predictions for any non-linear model ...... 165 C.1 Purpose: ...... 165 C.2 Procedure:...... 165

List of Tables

Table 1: ANOVA effects test results show significances of each test term as well as the whole model R2 from the nine selected quality metrics ...... 73 Table 2:Table outlining Tuckey HSD results to show differences between temperature levels for Drip loss, BMORS, and WHC where the effect of temperature was significant ...... 74 Table 3: Correlation matrix of quality attributes for chicken stored frozen at -10°C studied over 12 months...... 75 Table 4: Table of parameter estimates for the % drip loss Gompertz regressions (Figure2). Each parameter estimate is shown plus or minus the 95% confidence interval of the estimate...... 78 Table 5: Whole model (eq.1) ANOVA significance table for effects of temperature (- 10°C, -15°C, -20°C), time (weeks), and packaging type (OTR <0.05, OTR <0.1, overwrap) on beef patties stored frozen...... 107 Table 6: Parameter estimates and 95% confidence intervals for the TBARS regression to Modified Gompertz (eq.2) ...... 113 Table 7: Table displaying the coefficients used to fit each Gompertz parameters versus 1/K to polynomial relationship ...... 114 Table 8: Table showing first order exponential model (eq. 4) rate constant k for a* at 3 storage temperatures accompanied by RMSE for the fit of each temperature conditions...... 117 Table 9: Correlations between frozen chicken attributes across 12 months of storage, not separated by storage temperature: ...... 156 Table 10: Correlations between frozen chicken attributes across 12 months of storage, - 15°C storage ...... 157 Table 11:Correlations between frozen chicken attributes across 12 months of storage, - 20°C storage ...... 158 Table 12: Linear ANOVA results (α=0.05) wet basis moisture of ground beef ...... 161

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List of Figures

Figure 1: Diagram of prominent reactions and molecular species formed during lipid oxidation. Stages of lipid oxidation are presented: (1) the induction period, (M) the monomolecular phase, and (B) the bimolecular phase. Diagram originally presented by Schaich et al., (2013)...... 21 Figure 2: Percent drip loss at; -20°C● regressed with the Gompertz equation (eq. 2). Error bars represent standard error...... 76 Figure 3: Predicted drip loss % , Gompertz regression at -10°C, -15°C, & -20°C ...... 77 Figure 4: Predicted end of shelf-life (θ, month) based on a 6% drip loss quality limit with 95% confidence interval curves presented...... 79 Figure 5: Comparison of TBARS experimental data presenting three barriers OTR <0.05 (black), OTR <0.1 (gray with dots) & overwrap (white) through 11 months of -15°C frozen storage (n=3)...... 108 Figure 6: Comparison of redness (a*) experimental data presenting three packaging types L (black), H (gray) & O (white) through 11 months of -15°C frozen storage. (n=3) ..... 109 Figure 7: TBARS experimental data presenting cumulative averages of all packaging types and replications including three storage conditions -10°C (black), -15°C (gray with dots), and -20°C (white) through 11 months of storage (n=9). Different letter super scripts indicate statistical differences within months...... 110 Figure 8: Redness (a*) experimental data presenting cumulative average of all packaging types and replications including three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=9) ...... 111 Figure 9: Non-linear regression of TBARS results fit to the Gompertz equation (2). Plots A (-10°C), B (-15°C), & C (-20°C) demonstrates replication averages fit to predicted Gompertz model. Plot D combines the models from the three temperatures for comparison...... 112 Figure 10: Interpolated prediction of TBA# over one year for selected even temperature values ...... 115 Figure 11: Non-linear regression of redness (a*) to the three-parameter exponential model (eq. 4) ...... 116 Figure 12: Arrhenius relationship for the natural log of the rate of color degradation versus temperature ...... 118 Figure 13: One-year interpolated temperature predication for a* using first order Arrhenius kinetics (eq. 4, 5) ...... 119 Figure 14: Methodology flow chart including important mass (M) locations. “Ch” refers to chicken breast/meat mass “w” refers t moisture mass ...... 146 Figure 15: BMORS versus month showing three storage temperatures...... 147 xiii

Figure 16: WHC versus time grouped by temperature ...... 147 Figure 17: Maximum G’, solid-like storage modulus, versus month presenting three storage temperature conditions...... 148 Figure 18: Full width at half mass (FWHM) of average T2 distributions versus month presented or three storage temperatures...... 149 Figure 19: Group B Drip loss% versus month at three storage temperatures...... 150 Figure 20:Cook Loss% versus month at three storage temperatures ...... 151 Figure 21: Representative photograph of PAGE gel: ...... 152 Figure 22: MRI image showing a single internal slice of chicken (dark, solid-like) breast from T2 analysis. White (liquid-like) water standard is in bottom right while red hexagon shows the region of interest (ROI). T2 measures whiteness intensity of pixels¬...... 153 Figure 23: Histograms showing counts of bins representing T2 pixel intensity. Two distributions are shown: the average of the breasts stored at -10°C for 12 months and the frozen control breasts...... 154 Figure 24: Photograph of chicken samples showing white striping defect (left) compared with a typical healthy breast (right)...... 155 Figure 25: Wet basis moisture content of the <0.5 OTR vacuum bags presenting three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=3) ...... 162 Figure 26: Wet basis moisture content of the <0.1 OTR vacuum bags presenting three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=3) ...... 163 Figure 27:Wet basis moisture content of the open bags presenting three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=3) ...... 164 Figure 28: Non-linear, non-isothermal schematic describing the logic used for replacing the instantaneous rate of new process parameters onto a curve from the current process parameters in non-isothermal, non-linear modeling of quality progression...... 168

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Chapter 1. Introduction

Freezing is very old form of food preservation in certain cultures where cold climates froze food items and effectively extended the life span of the food. In the modern world freezing as a food preservation method became prominent in the mid-

1940’s. Freezing regulation was non-existent with processors freezing a wide variety of items with no real concern for freezing time or storage temperatures. This eventually led to a public aversion to frozen foods that were likely to be rancid and possible spoiled with mold. The Unites States Government realizing the potential in frozen foods established research center whose sole purpose was research freezing rate and storage conditions.

The Western Regional Research Laboratory (WRRC) conducted multi-year extended shelf-life tests on most food categories including beef and poultry from 1948-1965

(Ginsberg, 2002). The extensive research from the work of these scientist turned in to a multi-publication series coined Time-Temperature Tolerance studies (T-TT). The first instalment from Arsdel (1957) expressed the experimental focus of the series highlighting transportation, storage, and the unpredictable effects of temperature fluctuation. The conclusion made from the T-TT studies have provided Americans with safe and high quality frozen foods for the last half-century.

The T-TT studies are not without criticism however. The research, considering both economic feasibility as well as quality retention, established -18°C as the ideal

1 storage temperature for most frozen foods (Guadagni and Nimmo 1957). Technology in refrigeration, food preparation and handling have advanced since the 1950’s (P´erez-

Chabela & Mateo-Oyague, 2004). Furthermore, modern concerns encouraging a more sustaiable food supply have created a push for reduction in energy consumption. Keeping large quantities of meat and other foods cold is an energy intensive undertaking accounting for 60-70% of the electricity usage at a cold storage facility (Evans et al.

2014a). Lowering the energy consumption throughout the supply chain of foods is advantageous for both warehouse operators and the consumer. Evans et al., (2014b) report that a 1°C increase in temperature would result in a 3% reduction in energy consumption. Investigators have concluded that increased temperatures are accompanied by reduced shelf-life. Determining the optimal storage temperature based on the required storage duration and quality level is referred to as a practical storage life (PSL) ; (P´erez-

Chabela & Mateo-Oyague, 2004; James & James, 2006).

Freezing as a preservation method is well-known to extend the time a food can safely be consumed. It is also well established that freezing is not an absolute preservation method. The freezing process involves three steps: the chilling stage where the product meets the freezing point of the product, the phase change stage where ice crystal formation occurs, followed by the tempering stage which brings the food to an equilibrium with this storage room (Castro-Giráldez, Balaguer, & Hinarejos 2014). At the onset of the tempering phase an amount of liquid water highly concentrated with salts and other soluble materials exists and shrinks as the product temperature decreases. It has been shown that the partial pressure of the water in this phase of the frozen product

2 reduces exponentially with lowering temperatures and subsequent increased concentrations of solutes (Dyer et al. 1966; Storey and Stainsby 1970). The lowering of partial pressures is tied to the concept of water activity and is major explanation for while microbial activity does not occur in temperatures less than -10°C (Geiges 1996). Even still this unfrozen water fraction facilitates many chemical reactions tied to quality deterioration. Storage at -10°C is not likely but an optimal storage temperature above the current standard of -18°C likely exists.

1.1 Objectives:

The two connected studies described in this thesis share a common goal. This is to produce useful results for assessing the feasibility of increasing the temperature for the storage of frozen raw meat in the name of reducing energy usage. The specific objectives are the following:

1. Identify and implement quality attribute assessments of industrial and academic

interest for high and low-fat meat products

2. Understand and describe the effect frozen storage has on muscle food quality

attributes over time.

3. Describe the changes in quality attributes cause by temperature or packaging

variables.

4. Generate empirical predictive models as a function of the variables of time and

temperatures.

1.2 References:

Arsdel WB Van (1957) The Time-Temperature Tolerance of Frozen Foofd. 1.

Introduction-The problem and The attack. Food Technol 11:28–33

Castro-Giráldez M, Balaguer N, Hinarejos E, Fito PJ (2014) Thermodynamic approach of

meat freezing process. Innov Food Sci Emerg Technol 23:138–145. doi:

10.1016/j.ifset.2014.03.007

Dyer D, Carpenter D, Sunderland J (1966) Vapor Pressure of Frozen Bovine Muscle.

Muscle J Food Sci 31:196–201

Evans JA, Foster AM, Huet JM (2014a) Specific energy consumption values for various

refrigerated food cold stores. Energy Build 74:141–151. doi:

10.1016/j.enbuild.2013.11.075

Evans JA, Hammond EC, Gigiel AJ (2014b) Assessment of methods to reduce the

energy consumption of food cold stores. Appl Therm Eng 62:697–705. doi:

10.1016/j.applthermaleng.2013.10.023

Geiges O (1996) Microbial processes in Frozen Food. Adv Sp Res 18:1081–1083

Ginsberg J (2002) Quality and Stability of Frozen Foods Time-Temperature Tolerance

Studies and Their Significance. Am Chem Soc

Guadagni D, Nimmo C (1957) The time-temperature tolerance of frozen foods. II. Retail

packages of frozen peaches. Food Technol

James SJ, James C, Evans JA (2006) Modelling of food transportation systems - a

review. Int J Refrig 29:947–957. doi: 10.1016/j.ijrefrig.2006.03.017

P´erez-Chabela M, Mateo-Oyague J (2004) Frozen Meat: Quality Shelf life. Handb food

Sci Technol Eng Ch. 115:612–624. doi: 10.1016/j.jenvman.2014.01.053

Storey M (1970) The equilibrium water vapour pressure of frozen cod. J Fd Technol

5:157–163

Chapter 2. Review of Literature

2.1 Phenomena of sub-freezing water in food

The freezing process involves three steps: the chilling stage where the product meets the freezing point of the product, the phase change stage where ice crystal formation occurs, followed by the tempering stage which brings the food to an equilibrium with this storage room (Castro-Giráldez, Balaguer, &Hinarejos 2014)

2.1.1 Unfrozen water content in food

The amount of unfrozen water in a food is related to the concepts of freezing point depression. The composition of solutes in the remaining liquid water fraction effects food properties by altering the volume of the liquid water fraction (Chen, 1985).

Computer simulations made it possible to estimate the liquid water and ice fractions of many different foods based on the theoretical calculations connected to the freezing point depression and product compositions (Hsieh and Lerew, 1977). It is the liquid volume fraction which facilitates the reactions responsible of much of a food’s deterioration during freezing. Boonsupthip and Heldman, (2007) provide variations of the relationship between unfrozen water and product composition. They along with others have identified relationships between these parameters and water activity to provide scientists a relationship between a known metric of food stability and product temperature (Chen,

1987).

2.1.2 Recrystallization in frozen foods

Ice crystals are formed during the phase change while the quantity and volume are effected by the rate and final temperature of freezing (Bahuaud, Mørkøre, Langsrud,

2008). Slower freezing causes more damage by producing larger ice crystals which grow into the extracellular space. The volume of unfrozen water left behind is able to facilitate microbial growth and chemical reactions as well promote recrystallization of the ice present (Molina-García, Otero, Martino, 2004). Over time frozen foods will experience a shift in the quantity and size of the ice crystals present. The rate of this transition is temperature dependent and follows Arrhenius kinetics. Small ice crystals slowly melt while the excess moisture aggregates on the large ice crystals. The liquid water fraction volume remains mostly unchanged but the processes of melting and refreezing facilitate movement and fluctuations in local salt concentrations (Martino and Zaritzky 1989). Ice recrystallization is associated with protein denaturation due to the ionic shifts and the ice crystal growth damaging cells. The denaturation of myofibrillar proteins then has a macroscopic effect on drip loss and other water holding attributes (Zaritzky 1988).

2.1.3 Glass transitions state

Meat is a complex matrix of fat and protein but (in the context of frozen storage) most importantly water and ash (minerals, sugars and other small molecular weight

(MW<1000) solutes). The ash is a small percentage of the total mass of a product but it is responsible for the freezing point depression observed in all foods as well as the existence of unfrozen water in products stored at freezing temperatures (Boonsupthip &

Heldman 2009). This unfrozen water mass inside a frozen piece of food creates a system of high concentration solutes that is similar to a low moisture dry food at room temperature. An important property of low moisture foods is the glass transition temperature (Tg). The glass transition temperature of a matrix is critical to understanding the long-term stability of that matrix. Tg describes when the liquid water component of the matrix becomes more glass like that liquid like. When the viscosity of a liquid becomes so high the deformation due to gravity is no longer perceivable it is considered a glass (Kasapis 2006). The effect of this reduction in fluidity has a profound effect on many aqueous reactions.

Glass transition temperatures for beef have been broadly estimated over a range from 1° to -50°C. Brake and Fennema, (1999) conduct research with a differential scanning calorimeter (DSC) to create a reproducible method for an apparent Tg assigning

-13°C to as beef’s Tg. The reason for this discrepancy is thought to be the result of two glass transition temperatures. The -13°C Tg is thought to be associated with the interactions of water molecules with the macromolecules in the system. Whereas, glassy states observed at low temperatures are associated with the state of the water interacting with the solutes (Brake and Fennema,1999). Akköse and Aktaş (2008) described the Tg as the point where melting begins on a DSC exotherm. This group confirmed -13°C as a

Tg for beef. Although standards dictate -18°C storage for frozen meat is not unreasonable for product to experience -13°C during shipping or power outage. This finding further suggests the importance of maintaining frozen storage temperature.

2.2 Water holding capacity in muscle foods: measurements and deterioration

Water holding of muscle foods, unlike fruits and vegetable, is dominated by the protein structure and functionality that make up the muscles (Puolanne and Halonen

2010). In muscles 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 (Pham and Mawson 1997).

2.2.1 Water holding in meat processing (yield and freezing rate)

Water holding capacity (WHC) in meat has been described in literature as a muscles ability to hold water during the application of an external force. Both direct financial value and consumer acceptance is lost from meats with a poor ability to hold water (van Laack, 1999). Upon reception of raw meat, a further processor will have to purchase the mass of fluids exuded during the transportation or thawing process.

Processes such as brine injecting and/ or tumbling may be used to increases yield, color, and juiciness while also increasing microbiological stability (Alvarado and McKee 2007).

Drip formation and cook loss are the two most important water holding factors to food processors and both have downstream effects on the sensory properties mentioned. There are many factors that influence the ultimate water holding of a muscle and these start before the animal is brought to slaughter such as species, breed, age, feeding, pre- slaughter mood and many more (den Hertog-Meischke, van Laack, & Smulders 1997).

These factors are essential to meat quality. However, the focus of this review will be on the combined effects of freezing, storage, and brine to maintain water holding attributes. 9

Quality monitoring is especially important when freezing and storage processes are involved. Miller, Ackerman, & Palumbo (1980) found meat loses functionality as frozen storage continues and that after 7 weeks an experienced taste panel was able to detect differences in frozen sausage. The changes occurring in meat leading to a loss in functionality such as water holding and gel formation are a direct result of freezing, storage, and thawing. Functionality can be optimized during the freezing processes; fast freezing in pork (<120 min) provided post thaw drip similar to fresh pork whereas slow freezing (>240 min) showed an increase in drip formation (Ngapo, Babare, Reynolds, &

Mawson, 1999). Thawing also has been implicated as a step to reduce purge formation.

However, Gonzales-Sanguinetti, Anon, & Calvelo (1985), identified a reabsorption phase during purge formation that seemed to reduce the benefits of varied thawing rates.

Sigurgisladottir, Ingvarsdottir, & Torrissen (2000) highlight the shrinkage of muscle fibers as a primary change in the microstructure due to freezing and thawing of smoked salmon. Further explaining this phenomena, Martino and Zaritzky (1988) have described the process of ice recrystallization during frozen storage and how it continually disrupts the microenvironments of unfrozen water in meat leading to further quality loss.

Marinating or brining is a way to incorporate salt, phosphate, flavor ingredients, and additional water into a meat product. The highly charged ionic matrixes bind to residues on the myofilaments leading to greater water holding. Different phosphates result in different effective functionality. Gel formation during cooking is also enhanced leading to greater water holding in the final product (Xiong 2005). While studying whole muscle frozen beef, Pietrasik and Janz (2009) found that samples injected with brine

10 before freezing retained higher consumer liking and purchase intent along with the samples producing less purge.

2.2.2 Mechanisms in water holding.

Reduced water activity is an inherent part of the conversion of muscle to meat.

Upon death respiration stops aerobic respiration ends causing the formation of lactate and the consumption of ATP increases H+ concentration lowering the pH. The reduction of

ATP prevents the breaking of the cross bridges formed between actin and myosin.

Actomyosin becomes the dominant species shortening sarcomere lengths in a process known as rigor mortis (Scheffler and Gerrard 2007). Along with rigor a declining pH decreases negative electrostatic repulsion within the myofibrils leading to shrinkage of the myofibrils; excess water is pushed into the intracellular spaces. An additional cause of water holding loss is the denaturation of myosin leading to a reduction of the head portion of the protein effectively bringing the filaments closer together (den Hertog‐

Meischke et al. 1997). An in depth review on the topic of muscle water holding by

Puolanne and Halonen, (2010) covers the topic well beyond the scope of this review.

They delve into the specifics of multiple proteins implicated in the water holding abilities of muscle as well as the interactions of many different ions found in or added to the system. The traditional and basic reasons for water holding are highlighted: electrostatic forces, capillary forces, and osmotic forces.

2.2.2 Other methods of measuring water holding

A standardized gravitational drip loss measurement was has been presented by

Honikel (1998). This method involved hanging a cut piece of meat and collecting the drippings for 24 hours. This method allows for drip loss accumulation measurements up to 7 days. Other methods have been proposed to shorten the assessment. Updike et al.,

(2005) added bine and excess water before a centrifugation to determine the muscles maximum water binding ability.

2.3 Color loss in meats: sensory considerations, measurements, mechanisms, & kinetics

2.3.1 Color perception and consumer acceptance

A visual response to food involves a light source’s interaction with the product followed by the processing of the reflected or transmitted light trapped in the consumer’s retina. A change in the light source or individual will affect the psychophysical response to the same food stimuli (Meléndez-Martínez, Vicario, & Heredia 2005). Especially in red meats the color of the product is an initial indicator of the quality of the product.

Consumers rely heavily on this first impression by sight (Troy and Kerry 2010). While the actual eating experience the customer is not necessarily linked to the appearance of the product (Carpenter, Cornforth, & Whittier, 2001). However, when considering long term frozen storage color may be linked to other meat quality parameters. Connections

12 between browning in beef have linked myoglobin and lipid oxidation the rates of which are both effected on the presence of oxygen (Watts 1954).

2.3.2 Instrumental measurement of color

It is true that the setting a food product is found and the individual who views the product will both have significant effects on the perceived quality of a similar product.

However, when conducting color analysis is its essential to keep both factors constant. In this case the setting refers to a light source which is usually an essential part of a colorimeter or it is a part of an apparatus constructed for photo analysis. The individual is replaced by an electronic optic sensor attached to a computer system for interpretation

(Wu and Sun 2013). A colorimeter was the primary instrument used for product examination in the experimental sections however camera image analysis will also be discussed.

Colorimeters are considered the traditional method for surface color analysis in foods. A typical colorimeter used in the food industry is designed to mimic the perception of an average human eye (McCaig 2002). The collection of primary, light from the light source, or secondary, light reflected or transmitted, from an object are collected by a colorimeter. The values produced are then optically obtained by this collection without mathematical transformation (Meléndez-Martínez et al. 2005). Colorimeters typically output in the tristimulus YYZ or CEIL*a*b* color spaces. The major drawback of instrumental analysis is the small area used for measurements.

Cameras are typically inexpensive when compared to a colorimeter. The use of a camera for product analysis requires a specialized light box to ensure uniform light 13 distribution of the product. Also, the distance from the product must be fixed. Cameras capture light in semiconductors in photodiodes which are set up to measure light intensity and correspond to a specific pixel. The resulting image will typically be in a the RGB color space (Wu and Sun 2013). Converting accurately from RGB color space to

CEIL*a*b* requires computer software, such as MatlabTM, and specific algorithms outlined by León, Mery, Pedreschi, & Leon (2006).

2.3.3 Myoglobin oxidation kinetics effects on meat color

In the meat industry colorimetry has long been an important practice for quality control and analysis. It has been well document and observed that freshly cut red meat will appear deep purple then after a time exposed to air bloom will occur as bright cherry red color surfaces. This is typically followed by browning or graying of the meat. This dynamic color shift has been tied with myoglobin’s interactions with oxygen and its oxidation state (Mancini and Hunt 2005). Quality concerns arise when a “fresh” piece of meat begins to brown prematurely. The increasing presence of metmyoglobin through the oxidation of myoglobin, correlates negatively, R2=0.73, with consumer’s intent to purchase (Greene, Hsin, & Zipser 1971) Manipulations of the gaseous atmosphere interacting with meat products has provided shelf-life extensions to deter oxidation and browning (Venturini, Contreras, Sarantopoulos, & Villanueva, 2006).

Early studies of iron containing proteins in blood and muscle isolated myoglobin and hemoglobin to study their oxidation the metmyoglobin and methemoglobin forms, respectively. These processes were found to consume oxygen and operate under first order kinetics (George and Stratmann 1952). 14

The reactions of myoglobin have a very pronounced effect on red meats but color measurements have found use in poultry products as well. Lightness, L*, measurements have been a useful means of determining pale soft exudative (PSE) prevalence based on pre-slaughter handling (Bianchi, Petracci, & Sirri 2004)

Even during frozen storage, over time myoglobin will autoxidize and brown; reports show oxidation occurring at -80°C ( June, Ochiai, Hashimoto, 1985). Myoglobin behaves unusually in a frozen system exhibiting reverse kinetic behavior. Early observations show a distinct increase in myoglobin oxidation rate from -5°C to around -

15°C (Brown and Dolev 1963; Zachariah and Satterlee 1973). Later studies using a beef whole sarcoplasmic extract reports oxidation rate increases to -20°C before rates begin to decline as expected ( Frelka, Phinney, Wick, Heldman, 2017).

Myoglobin oxidation has shown temperature and oxygen dependence in studies comparing surface color of previously frozen meat. Bhattacharya and Hanna (1989) studied frozen ground beef while measuring total color change. They attribute most of the change in color to be from a loss in redness but also note a general trend of a reduction in total color during frozen storage. Also, of note they show little differences in color loss between vacuum and non-vacuum sealed beef. They do, however, see large changes in color loss rate based on initial fat content. Both zero and first order reaction kinetics have been used to describe the progression frozen storage. (Chen, Singh, Reid, 1988), using zero order kinetics reported an activation energy of 86 Kj/mol using zero order kinetics.

Bhattacharya and Hanna, (1989) described the reaction with a first order relationship but did not study an effect of temperature.

Early studies about interactions between fatty acids and heme containing proteins indicate a strong influence of reaction rates. It was shown oxygen consumption and heme protein destruction were greatly increased in the presence of polyunsaturated fatty acids.

(Haurowitz et al.) The heme protein was suspected of aiding initiation while also being attacked during propagation. This a complex and highly detailed area of study which has been outlined in reviews. Faustman, Sun, Mancini, Suman, (2010) reviewed articles confirming a strong connection between these important reactions but also points out multiple studies providing a contradictory data. Oxygen pressure is pointed to as the primary factor implicated when the associations between lipid and myoglobin oxidation are not linked.

2.4 Texture analysis of chicken breasts

The texture of cooked meat, especially tenderness, is a primary driver for consumer acceptances and overall quality (Deatherage and Hamm 1960). Properly quantifying tenderness and relating it to consumer acceptability in meat has been a concern for producers and academics for a century. As described in his thesis, Bratzler,

(1932) created a mechanical machine to test the shear force required to puncture meat.

Multiple shapes and sizes of probes were used and correlated with a palatability test’s

“tenderness factor”. The common “Warner-Bratzler” (WB) shear analysis is based on this early analysis. Later, Kramer, Aamlid, Guyer, & Rogers, (1951) developed a mechanical multi-blade analysis for general compression and shear analysis of foods. This method became known as the “Allo-Kramer” (AK) shear analysis and was used in poultry and other meats. Since then strides in automation and mechanics have greatly increased the 16 speed, ease and reproducibility of testing tenderness in foods including chicken breasts.

Despite new machines the present mechanical methods require extensive sample preparation. Researchers began development of a more user-friendly tenderness assessment for chicken known as BMORS (Lee, Owens, Meullenet, 2008a). Mechanical methods such as these are the most common ways to measure texture while novel measurements using lasers and infrared analysis are beginning to be developed.

Cold shortening, the shortening of muscle, is well known to impact tenderness negatively. Cross, West, & Duston (1981) analyzed microscopic methods for measure sarcomere length and compared them to a novel laser diffraction method. They report the laser method producing almost 10 times the throughput for with minimal loss to precision over the microscope method. Researchers connected to the beef industry were trying to find a non-destructive measure of toughness. Park, Chen, & Hruschka., (1998) showed correlation between near infrared (NIR) reflectance absorption and WB measurements.

Meullenet, Jonvillen, Grezes, and Owens (2004) later showed a reasonable correlation with NIR and a new mechanical razor blade shear method. While both sarcomere length and NIR show promising correlations with traditional mechanical methods they are unlikely to take their place as they require special equipment and analytical tools.

Work on the razor blade shear analysis was largely conducted by a single group of researchers over an almost a 20-year period. Their analysis covered many facets of poultry quality assessment along with the comparisons to proven shear force measurements and sensory analysis. Initial research for the razor blade shear analysis

(RB). Measuring shear force on breasts from of different weights and gender, both the

RB and AK tests did not show a difference but the methods hardness measurements correlated (Cavitt, Meullenet, & Gandhapuneni, 2005). In a separate study multiple sensory parameter including initial hardness were determined along with instrumental hardness measurements (AK and RB) and sarcomere length. The AK method correlated the least with sensory scores. The razor blade shear force (RBF) and razor blade shear energy (RBE) were both determined from the results and the RBE was found to be a better predictor of hardness scores (Cavitt, Youm, & Meullenet, 2004). Cavitt Meullenet, and Xiong, (2005b) continued their work in with another study comparing the RB, WB, and AK method a to sensory analysis. In this case they were unable to differentiate between the instrumental methods. However, the RB method was selected as the superior method since the extra sample preparation need for the AK and WB would take twice as much time. From the early research done on the RB shear method findings show clear correlations with sensory panels some studies even showed superior relationships than the traditional AK test.

The study that likely coined the name, Meullenet-Owens shear force (MORS), for this new razor blade method and also vetted a blunt razor method as well (BMORS)

(Lee et al. 2008a). The study focused on the optimization of the new razor blade method.

The analysis compared the two probes, both data analysis techniques, and while also determining the ideal amounts of punctures. The major finding is that BMORS is a reliable replacement for MORS. This is important from a cost stand point; the BMORS probe does not require replacement like the MORS razor blade. MORSF and MORSE

18 measurements were selected by this group to measure tenderness during long term freezing (Lee Saha, & Xiong, 2008b).

2.5 Lipid oxidation: mechanisms, history, and detection methodologies

2.5.1 Lipid oxidation Introduction & reaction progression

The goal of lipid oxidation research in foods has been to extend the shelf-life of products where microbiological risks are not a great concern. Most advancements have been discovered and implemented due to a better understanding of basic reaction mechanisms that drive the eventual off-flavor/odor development. Researchers have developed chemical detection methods allowing for a better understanding of extrinsic factors which influence lipid oxidation progression. These techniques have allowed for the validation of preventative practices such as oxygen exclusion and antioxidant addition used to slow the oxidative reactions.

The set of reactions involved in the oxidation of lipids, oils, fat, phospholipids, or lipoproteins are often referred to as peroxidation. This name comes from the role peroxides play to extend and create the chain reaction steps that propagate oxidation throughout a system. Peroxidation in fats and phospholipids is primary concern for meat scientists.

Lipid oxidation is a highly complex set of reactions which have been reviewed in depth in the past. The three phases which describe the general reaction scheme will be described briefly. Oxidative stress on a biological system containing polyunsaturated

19 fatty acids (PUFA) will result in the initiation of lipid peroxidation. The oxidative stress will manifest in the formation of free radicals or reactive oxygen species which readily react with PUFA. The double bonds present on a PUFA result in weakly bound hydrogen in the allylic position which is easily abstracted by the oxidant forming an alkyl radical.

Alkyl radicals (R●) form a conjugation with the existing double bonds on the fatty acid tail which leads to variability in later product formation. In the presence of excess oxygen

(O2) the radical fatty acid tail will interact and form a peroxyl radical (ROO●). This peroxyl radical can easily abstract a hydrogen from a nearby polyunsaturated tail beginning the propagation phase of the reaction. The reaction rate for the abstraction of further allylic hydrogens by a peroxyl radical is sufficiently slow for an antioxidant to hydrogenate the radical preserving the allylic hydrogen. After a successful abstraction the peroxyl radical forms a water molecule and an alkoxyl radical (RO●); an alkyl radical is left on the abstracted PUFA for peroxidation pathways to begin again. Alkoxyl radicals are highly reactive and can react with and create radicals from susceptible groups (R-H and R-OOH). Alkoxyl radicals can also undergo β-scission to produce short chain radical species; an exponential increase in oxidation rate is observed from this phenomenon.

These short chain molecules are susceptible to further oxidation and deterioration which ultimately form many of the volatile species associated with lipid oxidation off flavors and odors. (Labuza and Dugan 1971; Buettner 1993; Kamal-Eldin and Min 2003; Barden and Decker 2013).

Work to understand lipid oxidation will continue to evolve. Schaich, Shahidi,

Zhong, & Eskin (2013) have outlined a plethora of reaction pathways and various

20 products which may form depending on specific conditions. While not disputing the well- known phases of oxidation which include the initiation, propagation, and termination the group goes a step further to classify phases of lipid oxidation reactant production. The phases are outlined at the top of Figure 1. This includes the induction period where little change can be detected. The next phase is the monomolecular phase where single hydroperoxide molecules are formed and decompose independently. The last phase is the bimolecular stage where hydroperoxides are rapidly formed and decompose in pairs to for water radicals and a water molecule.

Schaich et al., (2013).

2.5.2 Past reviews of lipid oxidation in meat research

Lipid oxidation has been tied to meat quality attributes for decades. Many researchers have connected the off-flavors of shelf-stable and frozen meat products to the complex reactions involved in the oxidation of the lipid fraction of the products. Lipid oxidation is primarily a quality concern that may render a meat product unsatisfactory even before microbial risk factors begin. The study of oxidation in meats have been the subject of multiple literature reviews over the years. An early reviewer who gathered detailed mechanisms and the resulting acid species from oxidation suggested a human could detect rancidity in a product with only 0.1% of the fat having undergone a reaction.

This review discussed detection techniques for oxidation products including infrared spectrophotometry for measuring trans-acyl peroxides and polarogphy to study water insoluble peroxides (Morris 1954). Later a general review of the basic composition of meat fats, chemical mechanisms, and the measures to control lipid oxidation were presented concisely by Love and Pearson (1971). Morrissey, Shpheehy, Galvin, Kerry,

(1998) provides a useful description of the mechanistic role Fe2+ plays in the formation of reactive oxygen species in meat products. Also, the discussion includes the usefulness of antioxidants in an animal’s ante-mortem diet especially the use of carotenoids. Recently, researchers included lipid oxidation in a review of a larger discussion of the quality changes in refrigerated then frozen meats. Discussions point to the difficulty and inconsistencies in relating lipid oxidation measurements and consumer acceptance of product. It is also mentioned that lipid oxidation is considerably faster during chilled storage than frozen storage ( Coombs, Holman, Friend, Hopkins, 2017). Outside of the 22 meat focused reviews, lipid oxidation has been studied very broadly. Oil chemists have shown great interest in the reactions as long if not longer than meat scientists. Choe and

Min (2007) are an example who provide an in depth look at frying oil chemistry. Much of the basic knowledge learned from study of oil oxidation applies to fat oxidation in muscle systems. Choe and Min (2007)presents the relationship a product’s water activity has on lipid oxidation rate. The focus of the present review is primarily on fat oxidation in muscle systems but is also concerned with the reactions which occur at frozen temperatures. This latter consideration requires a level of creativity as many of the thermodynamics and kinetics which are relied upon at room temperature become shifted, altered, and are unknown at freezing temperatures

2.5.3 Detection of quantification of lipid oxidation in meat

Detection of quantification of the substances that lead to off odors and flavors because of lipid oxidation in food began in early in the 20th century. Early chemical assays required fine tuning over decades and still have many criticisms. Advanced technologies including chromatographic methods and mass spectrophotometry (MS) have been coupled together to allow for accurate and precise identification and quantification of many molecules formed due to lipid oxidation (Park et al. 2007) . Nuclear Magnetic resonance (NMR) imaging has even shown success in identifying oxidation products

(Falch, Anthonsen, Axelson, Ausrand 2004). Barriuso, Astiasaran, & Ansorena, (2013) have compiled a strong review of many methods which have been utilized for the study of lipid oxidation in foods. Including the ones previously mentioned as well as more 23 basic analysis such as Thiobaribituric acid reactive substances (TBARS) and Peroxide

Value (PV). PV works due to the predictable color change associated with the oxidation of iodine in the presence of peroxide. The method is useful in measuring oxidation throughout the monomolecular phase and in the early stages of the bimolecular phase shown in Figure 1. The TBARS method is known for quantifying malondialdehyde but is general measure of aldehyde containing molecules which are primarily considered secondary oxidation products ( Csallany, der Guan, Manwaring, Addis, 1984). TBARS ultimately is a quantification of the later stages of the biomolecular phase shown in

Figure 1. TBARS will be the focus of the remainder of the review as it has been a widely used measure for general lipid oxidation in meat products and was the selected method for shelf-life modeling during the experimental work. Alternative methods for measuring lipid oxidation will be discussed for comparison to TBARS results to provide a better understanding of the oxidation processes.

2.5.4 TBARS history and distillation method development

The TBARS assay is a non-selective measure of the state of oxidation in meat.

However, it has been used for decades due its reliability and simple sample preparation and procedure. It is reliable in that it will create reproducible data if performed correctly with the proper attention to detail. Two methodologies for the extraction of samples exist and within each results vary; the comparison of absolute TBARS value should be done with caution. Also, the actual meaning of the TBARS values are somewhat up for debate.

A very early report on the use of thiobarbituric acid (TBA) in a colorimetric assay referred to the target chemical simply as B. This report admitted to not knowing the 24 substrate for this reaction but speculated it was either an aromatic aldehyde, pyrimidine or both; likely a cellular metabolite found in the brain extract of rodents (Kohn,

Liversedge, 1944). Shepherd (1948) then used TBA to quantify a group of substituted pyrimidines selecting the assay for its simplicity and usefulness. The results from this report suggest that TBA is reactive with pyrimidine containing molecules. 2-

Sulfanilamidopyrimidine was selected as a typical highly reactive pyrimidine and was shown to produce a solution with a spectrophotometric absorption peak at 532nm. The investigators then utilized the TBARS analysis for oxidation measurement in foods.

Reports in oils, milk, and pork utilized the TBARS method for rancidity quantification

(Jennings, Dunkley, and Reiber, 1954). An investigation into the red pigment, absorbing light in the 532nm range became an important step in understanding the meaning behind early lipid oxidation analysis. Jennings, et al., (1954) attempted to study the red pigment formed chromatographically and then to determine the molecular weight of the pigment.

Unfortunately, this early attempt did not yield sufficient data to confirm the molecular weight of the pigment. Their studies, however, did isolate a red pigment in the 532nm spectrophotometric absorption range from oxidized milk, vegetable oils, malonaldehyde, and, 2-Sulfanilamidopyrimidine. This group speculated a 1:1 stoichiometric ratio for the analytes and thiobarbituric acid reagent combining to form the pigment with absorption in the 532 range. The connection of the TBARS test to oxidation in foods has allowed it be a tool for researchers and professionals in the food industry.

Despite many gaps in knowledge surrounding what the test is actually quantifying, correlations with rancidity intensity has solidified TBARS utility. Younathan

25 and Watts, (1959) established a correlation between a sensory panel’s rancidity score for the odor of cooked pork to the same samples TBARS value. Two cooking temperatures and the use of antioxidants produced various levels of oxidation in the samples. The same samples were then presented to judges and analyzed for TBARS. Analysis shows sensory scores ranging from 5.5 to 2 varying with TBARS values from 0.5 to 5.5 in an exponentially decaying relationship. The relationship is not perfect but a distinct cluster of values with low sensory scores and high TBARS values provide a good indication that the chemical analysis can capture rancidity and poor sensory acceptance. The study then compared the effect of curing and phosphates on oxidative rancidity in cooked samples.

This important result shows that curing and phosphate addition result in very low TBARS values compared to controls. Sensory scores agree with TBARs values in this case.

Nitrite reacts with ferric hemochromogen resulting in ferrous nitric oxide hemochromogen which is no longer a catalyst for lipid oxidation thus producing an antioxidant effect. Low rancidity persisted in these samples and color change does not occur until microbial spoilage took over. This group continued to produce valuable research in this area. In their next study, they reported that chloroform and methanol are the ideal solvents for the extraction of TBARS. Also, peroxide value analysis was compared with TBARS values in cooked pork to provide another dimension to the lipid oxidation analysis. The results show a rapid increase and plateau in TBARS values while peroxide value increases evenly. (Younathan and Watts, 1960). Moving forward

Tarladgis, Watts, and Younathan, (1960) produced the report on their heavily cited distillation method. The use of 1,1,3,3-tetra-ethoxypropane is used as the standard

26 reagent for this method. They point to the usefulness of the TBARS assay applied to muscle foods where phospholipids and protein bound lipids are highly present. Also they describe how TBA # or TBARS values do not translate from one method to the next pointing to variation in extraction methods. Tarladgis, Pearson, and Dugan, (1962) went on to investigate other compounds which react with TBA and possible alterations to TBA during heating in acid solutions. Further results from this report show TBARS values may be influenced by multiple factors during sample preparation and heating. These side reactions make preparation and execution vital if results are to be confidently correlated with taste-panels from previous studies.

2.5.5 TBARS extraction method development

The distillation method proposed by Tarladgis et al., (1960) became a standard procedure for TBARS determination in meat and is still used where higher sensitivity is required

(Witte and Bailey 1970). For most applications however, an extraction method which requires less equipment and is a simpler procedure can be used. Witte and Bailey, (1970) compared results from an extraction method to the standard distillation method. The new method incorporates a filtration step and removes the need for expensive distillation equipment. The two methods produced agreeable values and a regression of the relationship was produced. Salih, Smith, Price, & Dawson, (1987) also produced a regression between their modified extraction method and a distillation method showing the methods agree with an R2 of 0.91. They also suggest that the extraction method is less ideal for samples of fat greater than 10% due to contamination in the filtrate causing 27 turbidity. Overall, they report that the extraction method was “faster and easier.” Using a similar but modified extraction method Raharjo, Sofos, & Schmidt (1992) report a detection limit of 1nmo/ml meat extract corresponding to 0.77ng MDA/kg meat. Wang,

Pace, Dessai, Bovell Benjamin, & Phillips, (2002) decreased reaction temperatures to

40°C and increased the molarity of the TBA reagent to 80mM and focused on interfering agents in their analysis. Later, Kerth and Rowe (2016) improved the extraction method further with the implementation of a heated shaker for the 96-well plates used for the reaction. These results saw reduced variance by up to 6.5 times in the extraction method utilizing the heated shaker. Simplicity and repeatability have allowed the extraction method to all but replace the distillation method for TBARS quantification in meat and tissue samples.

2.5.6 Applications of TBRS in meat quality research

As mentioned previously it is hard to translate absolute TBARS values between and among studies (Tarladgis, et al., 1962). This is especially true for comparisons between the distillation and extraction methods but also for comparisons within methods.

TBARS should be considered to have low interlaboratory reproducibility. The relative change in TBARS due to an applied variable should be the primary use of the TBARS analysis. The numerous studies previously discussed are an important portion of the reports that helped in the development of the TBARS method. Nevertheless, many scientists have utilized the methods described above with modifications for lipid oxidation analysis in meats.

Marion & Forsythe, (1963) studies lipid oxidation evolution in raw turkeys over a seven day period using the distillation method. Red muscle, white muscle, gizzard, liver, skin, and heart were analyzed. They report that red and white muscle had significant differences between the rate of lipid oxidation formation; likely due to the higher levels of myoglobin in red muscle (Lawrie 1950). Phosphates were also studied as an antioxidant by Marion & Forsythe, (1963) which produced lower TBARS values with increases in phosphate concentration confirming observations from Younathan and

Watts, (1959) . Warmed-over flavor is a term that refers to the lipid oxidation perceived in cooked meat products. In a study to correlated TBARS and cooked flavor in dark and white chicken meat ten trained panelists evaluated warmed over flavor immediately after cooking then again after three days of storage. Regression analysis showed an R2 of

0.8699 between flavor scores and TBARS using the distillation method (Igene et al.

1985). These are two of many examples where the TBARS assay was successfully used for the detection of lipid oxidation and rancidity in poultry. Further examples of the use of TBARS in meat are reviewed in the following section on shelf-life studies of frozen meat.

2.6 Extended shelf-life studies of meat:

Understanding the progression of quality decline during frozen storage in meat products increases our understanding of mechanisms that dictate the shelf-life of meat products. The changes observed during freezing, discussed in section 2.2, create a situation where the chemical reaction kinetics which cause biochemical quality degradation at unfrozen temperatures will not transfer usefully to quality predictions in 29 the frozen state. Multiple groups have measured quality over limited time periods of meat in the frozen state using various experimental approaches.

Early attempts at studying quality change over long periods were focused on the effect of temperature fluctuations during storage. Bilinski (1981) studied the peroxide values and free fatty acid after 6 and 10 months of Pacifica herring stored at four isothermal temperatures and a set stored at -10°C or -18°C then transferred back and forth between the -28°C storage units every nine days. The products experiencing fluctuations saw PV and FFA values between the values of the products stored at the isothermal conditions (-10°C, or -18°C &-28°C). Both analyses were temperature dependent; the cycle treatment fell between the warm and cold set points with the -10°C cycle being greater than the -18°C cycle. Hagyard (1993) conducted a similar study for the effect of a single temperature fluctuations in lamb meat. Sample were moved once from one of three “warm” storage temperatures (-5°C, -10°C, -15°C) to a single “cold” storage temperature (-35°C) or vice versa at various time points. The group used flavor intensity from a sensory panel as a means of comparing sample. There findings showed significant differences between the warm first versus cold fist groups with the warm first being perceived to have great flavor intensity. This early work in extended shelf-life testing of frozen beef shows the responsiveness of meat products to storage temperature.

These studies did not attempt or allow for precise predictive analysis for non-isothermal storage.

A detailed study in to the effects long term freezing has on chicken breasts tenderness and quality parameters studied a single storage temperature (-18°C) (Lee et al.

2008b). In this study MORS and MORSE shear force, water-holding capacity attributes, color, and physical measurements were assessed bi-monthly for 8 months. This group used advanced statistical modeling and found their texture results fit a modified

Gompertz equation. They found cooked moisture content was inversely related to both drip (thaw) along with being inversely related to and cook loss. Color analysis showed poor correlation with the other assessments measured. Texture analysis of MORSF showed the strongest correlation to moisture content and cooking loss (R2=0.56,0.58).

Tenderness, drip loss and moisture content values increase up to month 4 then beyond that differences were not observed Lee et al., (2008b) and others provide a clear source for the expected results presented in Chapter 4. The results and trends observed here will be important for implementation of storage temperature as a variable on chicken quality.

The kinetics associated with frozen ground beef patties were measured by Chen, et al. (1988) studying drip loss, TBARS, and color over a 7 month period with measurements recorded every 2 weeks for 5 different storage temperatures. Drip loss and color were found to follow zero-order kinetics while TBARS was shown to follow first

2 order kinetics. Chen, et al. (1988) reported activation energies, Q10, & R for each quality characteristic. Bhattacharya and Hanna (1989) also studied frozen ground beef patties at

3 temperatures for 20 weeks. Drip loss, cook loss, and color were measured. First order kinetics were used to describe each attribute. The short time frame of this study likely did not capture then final stages of quality deterioration. These studies help shape the expectation for future temperature kinetic studies on the quality degradation of frozen raw ground beef.

Later frozen shelf-life studies did not share the temperature kinetic focus. Park, et al. (2007) studied volatile loss in pork sausage stored at -10°C for 120 days. They coupled this investigation with TBARS, Free Fatty Acids (FFA), and measurements for which were found to correlate with specific group of molecules (propane; 2,4-dimethyl-1- heptane; hexanal; 2-pentylfuran; 4-methyl-1-hexene). Vieira (2009) was interested in the effect of variable post slaughter aging times had on yearling beef. After aging two storage temperatures (-20°C & -80°C) were studied across 90 days with 3 sampling times. The samples aged longer showed that there was a decreased color intensity, decreased water holding capacity and increased bacterial counts after frozen storage. Recently, colleagues

Coombs et a., (2018), Holman, et al., (2018) and their team conducted a study mimicking whole muscle beef stored in a display case then frozen for consumption later on. They looked at various holing times (0, 2, 4, 6, 8 weeks) for the product at 4°C then for each of those discrete times the effect of various subsequent holding times spent at one of two frozen storage temperatures (-12°C & -18°C). Coombs et al. (2018) discussed the effects the variable storage times and temperatures had on the protein quality and oxidation of the meat showing more differences between the two groups stored at 4°C for different days than between either group stored at different frozen storage temperatures.

Holman, et al., (2018) focused on the lipid oxidation and free fatty acids in the product at various times throughout their design. They show a decrease in long chain fatty acids for the chilled samples, but the trend does not seem to follow after frozen storage.

Ultimately, they show very few trends in any of their analytics associated with lipid

32 quality over the course of their study. The work done by this group show the importance of quality raw materials and proper handling prior to frozen storage.

2.7 Magnetic Resonance Imaging (MRI) as a tool for meat analysis

Nuclear magnetic resonance (NMR), the technology employed in magnetic resonance imaging (MRI) provides scientists a glimpse inside a food. This technology relies on the magnetic properties of the protons which makes up the material. A sample is placed inside a strong magnetic field, a second magnetic field is applied, then turned off and the time for recovery of magnetization to the original field direction is recorded.

MRI analysis produces grayscale images where each 2-D pixels or 3-D voxel may be produced. The gray scale pixel value is associated with and described the liquid-like or solid-like nature of the atoms which make up that portion of the food material. Compared to solids, liquids will take a longer time to recover the original magnetization. Various

MRI scans exist to capture and create a robust analysis. Different scans include T1, T2, proton density. T1 and T2 are time constants for the vector components which make up the sum magnetic field for the atoms which are then summed within each pixel or voxel.

T1 corresponds to a time constant observed from the longitudinal component’s relaxation time. For longer times will be associated with higher grayscale values and will look whiter on an image. T2 corresponds to the time constant for the transvers component of the same magnetic field. Longer times, T2, will also appear white. Proton density measured before relaxation of the components begins and as the name suggest measures the amount of protons in a unit area (Lipton 2014).

MRI has been used in meat analysis to learn about the water mobility, diffusion properties, and spatial distribution. A major economic and sensory enhancing technique in the meat industry is the addition of brine. Brine is often injected but tumbling the meat in the presence of brine is popular in the poultry industry. The brining process has multiple benefits the salts and phosphates in the brine will enhance the water holding capacity of the muscles. MRI images have been used to monitor this transfer of water successfully. Histograms with bins representing pixel intensity showed an overall intensity increase as the tumble process proceeded. The images accompanying these histograms shows the tumble process was able to increase the overall intensity as well as create a more uniform intensity throughout. ( Dolata, Piotrowska, Wajdzik, Tritt-Goc,

2004). Researchers have also employed MRI and NMR in the study of cooked meats.

Chicken meat was cooked in an oven then subject to analysis. Analysis shows T2 intensity decrease after the completion of cooking. This is associated with loss of moisture. The study also conducted bulk NMR on raw chicken isolating 3 distinct distributions of water ( Shaarani, Nott, Hall, 2006)

Advanced computer science techniques are required for the analysis of MRI images. Algorithms have been developed to optimize the region of interest (ROI) (

Molano Rodríguez, Caro, Durán, 2012). Proper ROI placement is essential in MRI image analysis. The ROI is the isolated field of view that will be processed and analyzed.

Another valuable tool is database creation. Utilizing a database in MRI image analysis allows for rapid processing of multiple computer vision algorithms (ROI determinations) and the comparison to quality attribute data. Caballero Antequera, & Caro (2018) used

data mining on pork loins after MRI analysis and sensory attribute testing. Using multiple

computer vision techniques based on fractal dimension algorithms a match to multiple

sensory attributes was determined and a predictive relationship was determined. This

shows that MRI analysis can be successful in predicting consumer acceptance in meat

products.

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Chapter 3: The effect of variable frozen storage

temperatures on chicken quality and water holding

attributes

Abstract

Food is often frozen to prolong shelf-life by maintaining safety and high quality.

Since frozen food storage is energy intensive, careful evaluation of the influence of storage temperature on shelf-life is needed. Although the shelf-life of frozen meat at -

18°C may be desirable, the influence of slightly higher storage temperatures on shelf-life have not been thoroughly investigated. The objective was to evaluate the effect of storage temperature on frozen chicken quality attributes to identify improved energy efficiencies during storage.

Whole muscle chicken breasts (pectoralis major) were frozen to -20°C [-4°F] then stored at -10°C [14°F], -15°C [5°F], or -20°C for one year. In a randomized design monthly quality testing was conducted on three replicates thawed overnight to 4°C.

Quality analysis consisted of %drip loss measurements, water holding capacity (WHC), moisture content (WBMC), lipid oxidation by 2-thiobarbituric acid assay (TBARS), color, and cooked texture analysis by Blunt Meullenet-Owens Razor Shear (BMORS).

Differences in temperature conditions across time were observed in %drip loss,

WHC, L*a*b*, and BMORS (p<0.05). WHC analysis showed the highest brine uptake in the -10°C with all three temperatures decreasing over time. Drip loss, modeled with a 3 parameter modified Gompertz model (R2=0.70) showed significant differences in the asymptote parameters between temperatures. Sporadic BMORS results modeled linearly

(R2= 0.22) showing differences in temperature. TBARS values at all three temperatures were low and showed no change over time (p>0.05).

The creation of a shelf-life prediction model based on % drip loss results can be used to assess risk to processors considering increasing storage temperatures. TBARS analysis should be conducted on high fat meat to ensure quality can be maintained across various products. This study suggests energy savings may be accomplished without dramatic losses in quality by increasing storage temperatures modestly.

3.1 Introduction:

The low temperatures used in frozen storage accompanied by reduced water availability extend the products shelf-life by reducing the rate of chemical reactions and effectively inhibiting microbial spoilage below -10°C (Geiges, 1996). Guadagni and

Nimmo, (1957), working on the time-temperature tolerance studies (TTT), concluded that

0° F (-18°C) was the optimal storage temperature. Both quality and economic considerations were included in this recommendation which came with a 1-year shelf-life limit. This recommendation is still used in the industry today (Frozen Food Handling and

Merchandising Alliance, 2009) .

According to the March 2018 Poultry Slaughter report from the USDA roughly

250 million pounds of chicken are frozen each month (National Agricultural Statistics

Service 2018a). Keeping large quantities of meat and other foods cold is an energy intensive undertaking accounting for 60-70% of the electricity usage at a cold storage facility (Evans et al. 2014a). Lowering the energy consumption throughout the supply chain of foods is advantageous for both warehouse operators and the consumer. Evans et al., (2014b) report that a 1°C increase in temperature would result in a 3% reduction in energy consumption. Investigators have concluded that increased temperatures are accompanied by reduced shelf-life, this concept is referred to as a practical storage life

(PSL) (P´erez-Chabela & Mateo-Oyague, 2004; James & James, 2006). However, recent

Studies indicate that reverse stability kinetics govern some reactions linked to quality in meat. Frelka, Phinney, Wick, & Heldman. (2017) determined the kinetics of metmyoglobin formation in a muscle extract showing a maximum oxidation rate at -

20°C. Exploration of the temperature region slightly warmer where this rate would fall requires exploration.

The present study uses chicken breasts to describe the changes in a whole muscle protein system during isothermal storage at -10oC, -15oC and -20oC for a year. The primary goal of the study is to identify the effect varied storage temperature as time progresses on quality attributes. A secondary goal is to define the relationships empirically and provide predictions of the attributes progression.

3.2 Materials and Methods:

3.2.1. Sample acquisition and freezing

Approximately 440 chicken breasts were acquired from a poultry processing facility. Whole muscle, single chicken breasts (pectoralis major) were selected for weight, 5-7oz, with the absence of quality defects. Breasts with noticeable hardness, white striping, or any other physical deformity were rejected. The breasts were vacuum seal in in a Multivac package. Samples were split into two batches then flash frozen in a

CO2 blast freezer at -45°C to an internal temperature of -20°C±2°C in 25 min. Samples were shipped under CO2 snow for approximately 10 hrs. to Ohio State University. The samples were distributed into one of nine frozen storage cabinets (Insignia NS-

CZ70WH6) equipped with A419 thermostat (Johnson ControlsTM, Milwaukee, WI.) to hold the product isothermally at either -10±1°C, -15±1°C, -20±1°C.

3.2.2 Sample storage and analysis:

The three temperatures examined monthly for one year created 36 unique condition (n=3). For each condition 3 storage cabinets, serving as replication storage units, held product concurrently. 48 breasts were randomly assigned to each storage cabinet. Monthly, four breasts were randomly subjected to two groups of analytics.

Group 1 of analytics included drip loss and Blunt Meullenet Owens Razor Shear

(BMORS). Group 2 of analysis included thiobarbituric acid reactive substances

(TBARS), Water holding capacity (WHC), color (L*a*b*), and wet basis moisture content (WBMC). 50

Control samples, time zero, were placed directly in a 4±1°C thermostatically controlled (A419 thermostat) refrigerator overnight after approximately 10 hrs. spent frozen. The analyses descried below were conducted the day following thawing after a

22-25 hr. hold in the refrigerator. Time zero samples and monthly samples were handled identically after thawing. Groups 1 and 2 were not analyzed on the same day but the analysis for all conditions and replications from a single month were conducted with in four days. Group 1: analysis occurred as described below for drip loss, cooking, and

BMORS. Group 2: two breasts were subjected to color analysis prior to the removal from the packaging. Packages were opened, breasts dried, removed of fat, cubed, then blended in a FP1800B food processor (Black and DeckerTM, Towson, MD.) for two 15 sec. intervals with stirring between. The homogenous blend was then used for WHC, TBARS, and WBMC. Chemicals were sourced from Sigma Aldrich (St. Louis, MO.).

3.2.3 Drip loss

Drip loss measurements were conducted on thawed chicken samples while the breasts were still vacuum sealed they were weighed. The bag was then opened, the exudate was removed, and both the breast and the bag were blotted dry with a paper towel. Both the bag and breast were weighed. Initial chicken weight and exudate weight were calculated to determine %drip loss.

3.2.4 Cooking and BMORS

After drip loss measurements were taken the new mass of the breasts were summed then the breasts were added to a tumbler (HUM 30, HobartTM, Troy, OH.) with 51

13% marinade (3.8% NaCl, 2.4% Superbind HB-CT phosphate blend (Innophos,

Cranburry, NJ). Tumbling occurred for 20 min. followed by a 70 min hold at 4°C.

Samples were then removed, vacuum sealed and cooked in a sues vide style cooker for 20 min. in a hot water bath at 80°C. Samples were then refrigerated overnight. The next day the samples were removed from their cooking bags and dried. BMORS analysis was conducted according to (Lee, Owens, & Meullenet, 2008a) with modifications. Using a

TA-XT2 with blunt razor blade attachment (17 x 11 x 2 mm) shear-force values of six locations on a single breast were measured. The load-cell was 25 kg, test speed was

10mm/s, and the penetration depth was 10 mm.

3.2.5 Color:

A Minolta CR-300 Chroma Meter was calibrated through the packaging material used to store the chicken. Before opening the product. L*a*b* color space values were assessed in triplicate. Total color change, ΔEij, was calculated for breasts at each time point (i) and temperature condition (j) with the following equation

2 2 2 ΔE푖푗 = √(퐿 ∗0− 퐿 ∗푖푗) + (푎 ∗0− 푎 ∗푖푗) + (푏 ∗0− 푏푖푗) Eq. 1

Where L*0, a*0, and b*0 are the average color scores for the time zero color values and

L*ij, a*ij, and b*ij are the color scores at each condition.

3.2.6 Water holding capacity

Water holding capacity was conducted according to the centrifugal method presented by Updike, Zerby, & Sawdy (2005) with modification briefly, a salt and

52 phosphate brine (1.4MNaCl, 0.01M sodium triolyphosphate (NaTTP), 0.03% Na Azide, pH7.6) was used. Blended meat samples from a temperature condition were weighed and kept chilled then brine was added to all samples randomly in a 3:1 ratio. Each sample was thoroughly mixed followed by incubation at 4°C for 1 hr. After incubation samples were placed in centrifuge at 35,000g’s for 35 min. at 4°C. After centrifugation supernatants were passed through cheese cloth into a tared centrifuge tube and the weight was recorded. Pellet weights were calculated, and the results were expressed as % brine uptake.

3.2.7. 2-Thiobarbituric acid reactive substances:

Thiobarbituric acid reactive substances were determined according to Wang,

Pace, & Dessai (2002), with modification. Briefly, 10 gms of blended sample were thoroughly mixed with 10 ml 7.5% trichloroacetic acid (0.1% EDTA, 1% propyl gallate).

The mixture was held for at least 20 min. then centrifuged at 35000 g for 35 min. at 4°C.

The supernatant was passed through cheese cloth and kept chilled. Thiobarbituric acid

(80mM) was mixed 1:1 with each sample prior to incubation at 80°C for 40 min. The spectrophotometric absorbance was read in triplicate at 535nm on an EpsonTM (Suwa,

Nagano Prefecture, Japan) microplate reader. A standard curve was created with tetraethoxypropane (TEP) to calculate TBARS.

3.2.8 Moisture content

Moisture content was determined using an alteration of (AOAC 1995)in which samples

(2.5-3.5g) were measured on to pans of known weight and recorded. The pans were then 53 placed in a moisture drying oven set to 110°C for 17 hrs. After drying the pan were weighed.

3.2.9 Data analysis:

Response variables were tested with a linear analysis of variance (ANOVA) to determine significant effects of storage time and temperature. Interactions of time x time and time x temperature were also included in the models. Tuckey honest significant difference (HSD) was used to compare differences among temperature conditions. For response variables with a significant time x time interaction non-linear regression modeling was utilized when appropriate to create a more specific prediction equation.

Empirical model fitting will be described in detail as part of the results section. The study was replicated three times (n=3); storage cabinets were the unit of replication, details on this execution are described in section 3.2. The ANOVA, correlation matrix and non- linear regressions were conducted with the fit model, multivariate, and non-linear regression platforms, respectively, in JMP®Pro 13.1.0 (© 2016 SAS Institute Inc.).

3.3 Results & discussion:

3.3.1 Evaluating the effects of time and temperature on quality parameters:

Based on ANOVA results BMORS texture did not have significant effect of time, but the temperature and the interaction of time x temperature was significant (Table 1).

As a whole data set the BMORS results didn’t change significantly but within the temperature there was change over time. A Tukey analysis, (Table 2), shows that each 54 temperature effect on storage was different than the other temperatures (α=0.05). The

ANOVA did not show significant lack of fit (α=0.05) but it also does not explain the variability in the model well with an R2 of 0.35. This indicates that the data is variable over all but the results of the effects test are reliable. As storage temperatures increase the shear tenderness of the samples decreases over time. With the time x time interaction being non-significant a linear fit of BMORS vs. time grouped by temperature was conducted. Linear regressions of BMORS vs. time (R2=0.25) was limited by the variable data seen in appendix A1, Fig. 15. The -10°C storage showed a significantly faster rate of

BMORS increase. However, the confidence intervals in the -15°C and -20°C slope estimates overlapped greatly. Furthermore, the rate of change of the -20°C storage condition was not statically different than zero. It is clear that storage temperature has an effect on the shear hardness of the breasts, but power for temperature prediction does not exist from this model.

TBARS showed no change over time and no change with temperature. TBARS, a measure of secondary lipid oxidation products, did not increase throughout the study.

TBARS values ranged from 0.04-0.17 mg TBARS/kg meat these values are in agreement with previous reports for raw chicken from literature (Lopez-Bote, Gomaa, & Flegal

1998; Bianchi. 2004; Conchillo, Ansorena, &Astiasaran, 2005). The connected fat that may exist on the edges of a chicken breast were removed from the meat before analysis.

Further explaining the low TBARS values over time.

Total color change (ΔE) from the time zero condition was greatest at month one and reduced with a linear trend over the course of a year. The magnitude of the

55 differences based on the ANOVA prediction was approximately 1 color unit and the spread of data around the regression line is large. Each individual color attribute was analyzed but was not presented due to very poor fit to any ANOVA model (R2<0.20) however the effects of time were significant. The raw data for the individual color attributes show the lightness L generally reducing over the first six months after which the values stagnate. Redness, a*, remains essentially constant with a slight upward trend over the last six months amounting to less than a color unit difference. The b* values begin to reduce greatly until after month 6.

Drip loss data shows a reasonable fit to the ANOVA model and the model had a non-significant lack-of-fit (α=0.05). The model analysis shows all main effects and interactions are significant. The breasts express about 3% loss after the first month of storage and depending on temperature will express between 5% and 10% after a year, with -10°C storage producing the most. Also, the Tuckey HSD (Table 2) differentiated the temperature groups from one and other. To better understand the differences among temperature groups, advance model fitting will be conducted and discuss further (section

3.2). ANOVA suggests linear regression is not the best model since both interaction terms are significant.

Both time and temperature significantly affect WHC for the 8 months period studied. Months 0-3 were not included due to a methodology shift to significantly increase the precision and accuracy of the test. Month four data was found to be the maximum water holding for all three temperatures with values decreasing at seemingly different rates for each temperature. Table 2 shows the Tukey HSD results for the

56 temperature conditions where the -10°C condition is different than the two colder temperatures. As with the BMORS results, linear regressions to determine the rates of change of WHC vs time at different storage temperatures were conducted. The slope parameters could not be differentiated. The power of this model will not allow for predictive abilities, but the results indicate an inverse relationship with drip loss across storage temperatures.

The WBMC data shows an inverse relationship with drip loss; moisture decreases for the first 6 months. There is also a pronounce increase in WBMC values during the last

8 months. The effect of time and the interaction of time x time were found to be significant. However, the model showed a significant lack of fit (p>0.005). The data does not allow itself to an analysis of temperature. Moisture content is expected to mirror drip loss however variability in moisture readings did not show this statistically. Drip loss values form a plateau that will be discussed further but the WMBC shows an increase at the over the last few months.

A correlation matrix for the -10°C results are presented in Table 3 a more detailed analysis of the effect temperature has on these correlation is presented in appendix A1:

Table 9Table 10Table 11. Correlation coefficients are expectedly low considering the variable data. In the colder conditions the correlation constantans become smaller and the significance reduced as less change occurred among all tested attributes. The best correlations exist between BMORS and drip loss (R2=0.57) in the -10°C condition

3.3.2 Drip loss shelf-life prediction model and analysis:

The trends of drip loss formation with time and temperature required specialized relationships that a simple linear model could not provide. The drip loss data appears to grow exponentially from time zero to a specific time point that is dependent on temperature. Drip loss increase slows dramatically at this point forming an apparent asymptote. The Gompertz model, was fit to the experimental drip loss data separated by temperature. The sigmoidal relationship is often used for microbial growth modeling

(Belda-Galbis, Pina-Pérez, & Espinosa., 2014; Hossain, Follett, & Vu, 2016) and has been implemented to describe texture change in cooked chicken (Lee et al. 2008b).

−푒−푘(푡−푡푚) 푦 = 푦푚푎푥 ∗ 푒 + 푦0 Eq. (1)

The modified Gompertz equation contains three parameter estimates where the maximum drip loss, ymax, approached by a given condition may be referred to as the upper asymptote. The rate (k) is the maximum rate through the exponential phase of the curve.

The inflection point (tm) determines the curvature of the model and is describe

푦 mathematically as the time when equals e-1(Phinney, Goode, Fryer, Heldman, & 푦푚푎푥

Bakalis, 2017)

The regression of the model through the -20°C data is presented in Figure 2 The drip loss data fits the Gompertz model better than the linear ANOVA model with a R2 of

0.71 and also produced and RMSE of 0.0136 which is sufficiently low. Table 4 outlines the parameter estimates and their 95% confidence intervals. Statistical differentiation between ymax parameters at -20°C and -10°C as well as between -20°C and -10°C was

58 observed based on confidence intervals not overlapping (Phinney et al. 2017). The rate and inflection point parameters do not show statistically different values from one temperature to the next. The drip loss formation shows approximately the same rate of growth but at warmer temperatures formation will continue for a longer time.

The Gompertz model will create a full sigmoid but, in this case, only the upper half of the curve is used to model the drip loss data. The inflection point Tm is reported between -0.02 and 0.67 and is not statically different than zero at any temperature. This means that about 36% of the curve exists before time 0. This approach was used as an alternative to an exponential fit to allow for better agreements with the values in months

2 through 4 .

For shelf-life predictions Arrhenius kinetics were not used considering the low confidence on each rate parameter and the variable plateau’s present from the model.

Shelf-life prediction modeling described by Fu and Labuza (1997) was used to relate time to reach end of shelf-life (θ) and temperature. The exponential equation bellow was used: ln(휃) = −푏푇 + 푐 Eq. 2 where c is the intercept or reference temperature, T is temperature in °C and b is the slope. For shelf-life modeling each freezer replication was regressed with the Gompetz model to obtain nine sets of parameter estimates. Equation 1 was solved as a function of time at a defined y or quality limit (QL):

푦 ln(l n( 푚푎푥)) 푡푖푚푒 (휃) = 푄퐿 + 푡 Eq. 3 −푘 푚

59 where the parameter estimates from each replication regression were used for k, ymax, and tm producing three end of shelf-life time, θ, prediction equations for at each temperature.

A shelf-life of 6% drip loss was selected as a quality limit (QL) based on anecdotal suggestions by a meat scientist professional. Using 6%, parameter estimates for Equation

2 were calculated: b= 0.15 and c = 0.30, R2 =0.94. Demonstrating actual end of shelf life in months, the curve in Figure 4 directly relates θ to temperature. 95% confidence curves were extracted from the regression of Eq. 2 using JMP®Pro 13.1.0’s “fit y by x” platform. This confidence interval translates to a ±1.86 month prediction window for θ at

-18°C.

3.4 Discussion:

3.4.1 The influence of product and handling on results.

To accomplish the goals of this study, namely to create predictions which can be related to protein quality under different storage conditions that is also applicable to an industrial audience, chicken meat was selected for analysis. Homogeneity within and between samples was very important. Chicken breasts were identified as the ideal whole muscle system for the study as they were expected to be physically and genetically similar. Pectoralis major muscles in poultry have a highly uniform fast-twitch fiber type indicating the protein make-up will be homogenous as well (Bandman and Rosser 2000).

Unfortunately, due to difficulties finding breasts with a low level of genetic quality defects a non-local supplier was used so the pre-freezing control and analysis was limited.

Certain attributes, especially pre-freeze mass, moisture, and color score were beyond the abilities of the study. Without the weights of the breasts before packaging the weight of the packaged product coupled with drying the bag and breast were used to indirectly measure the initial weight and exudate weight for drip loss analysis. The error inherent in the weighing processes were essentially doubled as these weights were re- used in calculations. A moisture analysis of the raw unfrozen breasts could be used to normalize the relationship between drip loss and moisture content. This would also require post freezing moisture contents to be analyzed before blending. In retrospect pre- blending moisture would be the idea method, allowing for outlier examination of the sub- replicate breasts. The results show the color change from month 1 to 12 months of storage was about 10% of the color change from the time zero to month one. This is an interesting observation in itself; it is brought up here to qualify the usefulness of initial color readings. The hope would be that analysis a delta color score at each time point could better explain the variability seen in the ANOVA model providing more confidence in the relationships with time and temperature.

A major oversight must be noted pertaining to the BMORS results. The samples were brined but the mass and moisture content were not measured prior to cooking. The effects seen in the texture results show trends with temperature and time but a large amount of the variability in the data is likely explained by inconsistent pick-up during the brine process. The results described above still provide information, but the covariate of moisture content throughout the procedures should be a target for future work.

3.4.2: The role of water in frozen muscle quality

Water is of central importance in frozen meat quality due to the interactions with the myofibrillar proteins in the thaw state and the chemical reactions it facilitates while frozen (Kiani and Sun 2011). The ability for a meat’s protein structures to hold or retain water is vital to physical and sensory quality attributes (Van Laack, 1999; Hughes,

Oiseth, Purslow,& Warner 2014). Water holding attributes such as drip loss, cook loss, moisture content, and the ability to up take brine are thought to rely on protein quality.

The mechanistic connection between the parameters is not well known and predicting one attribute based on the others has not yet been hugely successful. The parameters are of great interest to processors when yield increase is possible (Van Laack, 1999). Water holding plays an important role in visual appearance as well as darkening occurs after higher amounts of drip. Also, Tenderness, the most important attribute for sensory acceptance (Deatherage and Hamm, 1960), has been linked to water holding attributes.

Based on the current literature it is clear that the attributes linked with water holding are important and the level of quality is highly dependent on the myofibrillar protein’s structure and interactions with water (Hughes et al., 2014). Frozen storage will play an important role in the structures of the muscle and multiple interactions with water.

Research into freezing effects on whole muscle protein structures provides insight into predicting quality after storage. The practice of freezing has been shown to effect muscle structure. After measuring shrinkage in both never frozen and previously frozen salmon Sigurgisladottir, Ingvarsdottir, Torrissen, Cardinal, & Hadsteinsson, (2000) found the previously frozen filets to exhibit shrunken muscle fibers with more space between 62 them. Longer times characteristic freeze times have shown connections with increased drip loss formation (Añón and Calvelo, 1980). Slower freezing results in larger ice crystals forming within the matrix leading to cell rupture or disruption of the myofibrils.

This leads to the release of proteolytic enzymes which encourage muscle fiber denaturation and separation (Bahuaud et al., 2008). Reports also suggest ice crystals cause myofibrillar breakage (Kaale et al., 2011). Frozen foods contain an amount of unfrozen water which is dependent on the product composition and the storage temperature (Boonsupthip and Heldman, 2007). This small amount of liquid water has an increased ionic strength which along with facilitating oxidative reactions is thought to promote protein denaturation (Miller et al., 1980; Leygonie et al., 2012). Meat is a complex system made more complex by the additional transformation of water to ice.

Understanding this system is crucial to properly predicting and preventing unnecessary quality loss. The phenomena associated with freezing can both be understood better by and help us to understand the progression of quality loss.

Gonzales-Sanguinetti, Anon, & Cavelo, (1985) show that exudate formation is actually a two-stage process where initial drip forms then reabsorption occurs and continues over 24 hrs. This is a possible explanation for error in most reported drip loss data if thawing times fluctuated. The drip loss model presented in Figure 3shows a distinct relationship with storage temperature. Martino and Zaritzky (1988) shows a model for frozen beef where drip formation stops after about five months of frozen storage at -20°C with -10°C storage having reached the same point three months earlier.

Martino and Zaritzky (1988) relates the formation of drip in the beef samples with the

63 diameter of ice crystals as they recrystallize during storage. As time passes under frozen storage small ice crystals slowly become part of larger ice crystals until a maximum diameter, dependent on the product, is reached. This study only examined 5 months of storage where ice crystal sizes and drip formation at different temperatures coverage. In the present study the differentiation in plateau did not occur until month 6. From months

1-5 the drip loss was not very different among the temperature groups, some of the difference could even be attributed to fast thawing in the warmer conditions leaving more time for reabsorption during the second stage of drip loss formation.

3.4.2: Quality loss in a muscle system

Water is of clear importance to muscle quality and the results from this study show the interconnected nature it has with other attributes. Many attributes tested correlated with drip loss (Table 3) namely: WBMC, L*, WHC and BMORS. Ice recrystallization as well protein oxidation and denaturation likely play significant roles as these parameters progression.

WBMC results showed a negative relationship with the drip loss results.

However, no effect of temperature was observed. The correlation is not unexpected (Lee et al. 2008b). The lack of effect due to temperature is likely due to the variability in starting material being greater than the differences expected due to drip loss induced moisture change.

A slight darkening of breasts was observed as L* values decreased over the first 6 months of storage demonstrating a similar trend as drip loss results. The present study and Lee et al., (2008b) showed a poor but significant correlation between drip loss and 64

L* during frozen storage. This darkening may simply be attributed to a concentration of the muscular components due to the moisture loss accompanying drip formation. Chicken breasts stored frozen appear to exhibit a different relationship with breast lightness than the pale soft and exudative (PSE) quality defect. Correlations show a fresh breast with higher L* values producing more drip loss. (Allen, Fletcher, Northcutt, & Russell,

1998;).

In this study water holding capacity and BMORS results are greatly influenced by the addition of phosphate and extra moisture to the breasts. The group 1 breasts WHC results are incomplete but show a deterioration from month 4 onwards. It was not expected that the -10°C condition would absorb the most brine in this time. Considering the earlier discussions, the -10°C would have undergone the most protein damage while also thawing first and having the longest time to reabsorb its own drip. The specific brine uptake is not known but a similar distribution is likely present. This suggests an uneven array of moisture content in the BMORS samples. Even with the unknown and uncontrolled treatment the effect of temperature is still clear. The data is to disperse to provide predictive linear models, but the temperature groups are still distinguishable from each other. The results show the damage cause during freezing effect tenderness in a different way than water holding capacity. Lee et al., (2008b) showed a 30% increase in

MORS results after 4 months of frozen storage at -18. The present study found that the rate of BMORS increase over time for the breasts stored at -20° was not different than 0 .

While the 2°C difference should not be ignored, the differences in tenderness are likely attributed to the addition of brine.

While TBARS results do not show significant change over time, it is clear a small level of oxidation took place. In the product used, this is not a quality concern as the odor of the product was not noticeably off-putting by the technicians. However, the small level of oxidation observed likely increased the oxidation of the proteins (Haurowitz et al.;

Bekhit and Faustman 2005; Soyer et al. 2010). Myofibrillar proteins are susceptible to oxidation but the oxidation of myoglobin is the most noticeable outcome of this oxidation as browning occurs. Frelka et al., (2017) described the reaction kinetics for the oxidation of myoglobin which showed a maximum rate at -15°C under frozen condition. No effect of temperature was seen in the redness or yellowness loss which may be explained by the reverse stability relationship with temperature. Direct oxidation of deoxymyoglbin likely occurred prior to oxygenation observed by (Mancini and Hunt 2005). Lipid oxidation is not a direct concern in low fat foods, but its influence of protein oxidation and the color and other properties of the muscle may be vital.

3.5 Conclusions & Recommendations

Overall the results from the study were unable to be transformed into useful predictive models as a function of time and temperature. A compromise in temperature modeling allowed for shelf-life predictions with a reasonable range of confidence.

Creating models of this type can be useful tools for food processors. Determining practical shelf-lives at various temperatures is challenging in muscle foods with complex quality attributes. A major recommendation for future researchers is to reduce and measure variability where ever possible. For the creation of successful models as a 66 function of temperature measurements of all variability is key. The present study showed a temperature dependence and correlation between major poultry quality parameters such as tenderness, and brine uptake but the data was ultimately too dispersed for accuracy in predictions. The current explanation for muscle cell rupture by Martino and Zaritzky,

(1988) and the effect on drip formation does not agree with the drip formation results observed in the late stages of frozen storage. More research is needed to better understand what drives quality loss in the latter portion of meat’s shelf-life at various storage temperatures.

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3.7 Tables and Figures

Table 1: ANOVA effects test results show significances of each test term as well as the whole model R2 from the nine selected quality metrics

Quality Variables Time (month) Temp. (°C) Time x Time Time x Temp. R2

Drip Loss <0.00011 <0.00011 <0.00011 0.01361 0.67

WBMC2 0.02291 0.1905 <0.00011 0.9506 0.30

WHC (4-12)3 <0.00011 0.00021 0.04421 0.3191 0.46

BMORS4 0.4636 0.00011 0.1233 0.01031 0.35

ΔE5 0.00031 0.8644 0.2352 0.3855 0.16

TBARS6 0.2458 0.2075 <0.8980 0.6245 0.048

1statistically significant term, α=0.05

2WBMC= wet basis moisture content

Table 2: Table outlining Tuckey HSD results to show differences between temperature levels for Drip loss, BMORS, and WHC where the effect of temperature was significant

Tuckey HSD1 results Drip BMORS WHC (4-12) for temperature levels Loss -10°C a a a

-15°C b b b

-20°C c b b

1 results showing different letters indicate statistical difference between condition,

α=0.05

Table 3: Correlation matrix of quality attributes for chicken stored frozen at -10°C studied over 12 months.

DL WBMC L* a* b* BMORS

DL R 1 -0.46731 -0.2649 0.2886 -0.1002 0.56651

WBMC R 1 0.294 -0.1635 -0.0916 -0.27

L* 1 -0.2766 0.34561 -0.0486 a* 1 -0.0968 0.1524 b* 1 0.1441

BMORS 1

1statistically significant correlation, α=0.05

10% 9% 8% 7% 6% 5%

4% Drip loss % loss Drip 3% R2=0.71 2% 1% -20°C -20°C model 0% 0 2 4 6 8 10 12 Month

Figure 2: Percent drip loss at; -20°C● regressed with the Gompertz equation (eq. 2). Error bars represent standard error.

12%

10%

Drip loss % loss Drip 4%

2% -10°C model -15°C model -20°C model 0% 0 2 4 6 8 10 12 Month

Figure 3: Predicted drip loss % , Gompertz regression at -10°C, -15°C, & -20°C

Table 4: Table of parameter estimates for the % drip loss Gompertz regressions (Figure2). Each parameter estimate is shown plus or minus the 95% confidence interval of the estimate.

Temperature Gompertz (eq.2) model parameter estimates ±95%CI condition:

ymax (DL%) k (1/month) tm (month)

-10 9.9 ± 1.2 0.36 ± 0.19 0.67 ±0.74

-15 8.3 ± 1.3 0.31 ± 0.21 0.52 ± 1.02

-20 6.1 ± 0.8 0.45± 0.39 -0.021 ± 1.2

17 upper 95% CL 15

13 Lower 95% CL

month) 11 θ, θ, 6% quality 9 limit

End of shelf shelf ( of End life 5

1 -20 -15 -10 Storage Temperature °C

Figure 4: Predicted end of shelf-life (θ, month) based on a 6% drip loss quality limit with 95% confidence interval curves presented.

Chapter 4: Effect of storage time, temperature and

package on lipid oxidation and color of frozen ground

beef patties

Abstract

In frozen storage, beef often develops discoloration and off-odors. Typically, beef has a shelf-life of 6-12 months at -18°C [0°F]. Through the understanding of quality degradation reactions and their dependence on temperature, an argument may be made to encourage storage at a more sustainable temperature. The objective of this investigation was to monitor the effect of packaging and model the effect of temperature on lipid oxidation and redness loss in frozen ground beef.

In a completely randomized study 297 ground beef (73:27) patty units were stored at three temperatures (-10°C, -15°C, and -20°C) 11 months. Color and lipid oxidation data were collected monthly. Prior to analysis, meat was thawed at 4°C for 24 hours.

L*a*b* color scores were recorded using a Minolta CR-300 colorimeter. Lipid oxidation was quantified using 2-thiobarbituric acid assay (TBARS). Redness and TBARS values were modeled using JMP statistics (α=0.05).

Degradation of quality attributes did not occur differently under multiple oxygen permeable packages. Statistically, barriers were used as additional replications for a

80 robust analysis of temperature. The change in redness (a*) over time followed second order rate kinetics after modifications from a 3-parameter exponential fit (R2=0.93). The rate constant at -10°C (0.134 month-1) was higher than that at -15°C (0.027 month-1) and -

20°C (0.017 month-1) which were found to be different based on confidence intervals.

Arrhenius activation energy for a* was calculated to be 122.3 kJ/mol. TBARS data was fitted to a modified Gompertz model (R2=0.91). The lipid oxidation rate at -10°C progressed and more rapidly than that at -15°C or -20°C which were similar. Predicted maximum TBARS was dependent on temperature and greatest under -10°C, followed by

-15°C, and -20°. The state and availability of the unfrozen water may play a role in maximum TBARS observation. Similar rates in the colder temperatures provide an opportunity to reevaluate storage conditions for meat product composed of greater than

20% fat products.

4.1 Introduction:

In 2017 the United States froze and stored a record 536 million pounds of beef including ground beef, roasts, loins, steaks and all bone-in pieces (NASS), 2018. The low temperatures used in frozen storage accompanied by reduced water availability extend the products shelf-life by reducing the rate of chemical reactions and effectively inhibiting microbial spoilage below -10°C (Geiges 1996). Guadagni and Nimmo, (1957), working on the time-temperature tolerance studies (TTT), concluded that 0° F (-18°C) was the optimal storage temperature. Both quality and economic considerations were included in this recommendation which came with a 1-year shelf-life limit. This

81 recommendation is still used in the industry today (Frozen Food Handling and

Merchandising Alliance, 2009) .

Keeping large quantities of meat and other foods cold is an energy intensive undertaking accounting for 60-70% of the electricity usage at a cold storage facility

(Evans et al. 2014a). Lowering the energy consumption throughout the supply chain of foods is advantageous for both warehouse operators and the consumer. Evans et al.,

(2014b) report that a 1°C increase in temperature would result in a 3% reduction in energy consumption. Investigators have concluded that increased temperatures are accompanied by reduced shelf-life, this concept is referred to as a practical storage life

(PSL) (P´erez-Chabela & Mateo-Oyague, 2004; James & James, 2006). However, recent studies indicate that reverse stability kinetics govern some reactions linked to quality in meat. Frelka, Phinney, Wick, & Heldman. (2017) determined the kinetics of metmyoglobin formation in a muscle extract showed a maximum oxidation rate at -15°C.

Past studies have attempted to understand the effect of temperature on color loss in ground beef, the attribute most closely related to myoglobin oxidation, with inconclusive results (Chen, Singh, & Reid, 1988; Bhattacharya & Hanna, 1989). These researchers also attempted to differentiate change in Thiobarbituric acid reactive substances

(TBARS) at different temperatures. Therefore, the goal of this study was to investigate the association of TBARS and color change occurring at -10oC, -15oC and -20oC with high, medium and low O2 permeable packaging related to meat quality.

4.2. Materials and Methods:

4.2.1. Sample packaging

Commercially processed ground beef patties were produced from a single batch of 73% lean ground beef mixture. Frozen trim (approx. 50% fat) and frozen lean (approx.

10% fat) were thawed, ground (Weiler 1612, Mokena, IL.) then analyzed using an industrial IR spectrophotometer program (Tomra Sorting Solutions, QVision 500 Meat

Analyzer, Asker, Norway) to achieve the desired lean:fat ratio. After a final grind (Weiler

878) the meat was combined in a paddle mixer (Weiler M5000) then mixed for a set period. Square patties (60 mm x 60 mm x 6.4mm) were arranged in six layers of fifteen patties (3 x 5) separated by wax paper; a single patty, the observational units, may be referred to as sub-sample. The experimental units consisted of 90 patties packaged in one of three ways: plastic overwrap; a high oxygen permeability package, OTR <0.1 cc/100 in2/day; and a low oxygen permeability package, OTR <0.05cc/100in2/day. Both seal packages were gas flushed (75% N2, 25% CO2) had moisture transfer rates of <0.4 gms/100in/day. Four units of similar packaging method were placed in a cardboard box

(21.6 cm x 33.7 cm x 19.1 cm) for freezing and storage.

4.2.2 Product Freezing

After packaging all samples were stacked no more than one box high and separated then placed in a commercial frozen storage room (-24°C±2°C). The samples froze over 25 hours to an internal temperature of -21°C±2°C. Sample temperatures were recorded with Dickson® High temperature loggers (Addison, IL). The samples were held 83 for a total of three day then shipped frozen (< -18°C) approximately 5 hours to Ohio State

University’s campus.

4.2.3 Product Storage

At the laboratory samples were distributed into one of nine frozen storage cabinets

(Insignia NS-CZ70WH6) which serve as the experimental unit and basis for statistical replications. Each cabinet was equipped with a A419 thermostat (Johnson ControlsTM,

Milwaukee, WI., U.S.A) to maintain a more precise ambient storage temperature. The nine cabinets were evenly divided into three groups with each group being assigned a different set point. The three set points for ambient storage were chosen to be -10±1°C, -

15±1°C, or -20±1°C.

4.2.4 Sample Preparation

Time zero samples were placed directly in a thermostatically controlled (A419 thermostat) refrigerator overnight (24-26 hours) at 4±1°C. Time zero samples and each month’s samples were handled identically after thawing. Internal product temperatures were measured with an Omega RDXL4SD logger (Stamford, CT., U.S.A.), each unit was opened, and samples were removed. A single patty from each of the six layers was removed from the unit. The patty locations were consistent for all units. Patties were removed from the corner of the top and bottom layers, and central positions on the middle layers in an attempt to capture the most representative sub-sample. The surface color of the patties from the top three layers were measured according to the procedure below. Then the 6 selected patties from a single unit were combined in a FP1800B food

84 processor (Black and Decker, Towson, MD., U.S.A.) and blended lightly for TBARS analysis described below. During a month’s testing all -10°C samples were tested on the first week followed by -15°C samples which was followed by the -20°C samples the on third and final week. In a given week the storage cabinets were randomized by day then packaging barriers within the replication was randomized for each day.

4.2.4. 2-Thiobarbituric acid reactive substances (TBARS):

Thiobarbituric acid reactive substances were determined according to (Wang et al. 2002), with modification. Briefly, 10 gm of blended sample were thoroughly mixed with 10 ml

7.5% trichloroacetic acid (0.1% EDTA, 1% propyl gallate). Three sub-samples were collected from each blended unit and held for a minimum of 20 min. on ice. The mixtures were centrifuged at 35000 g for 30 min. at 4°C. The supernatant was passed through cheese cloth, centrifuged again and the second supernatant filtered through a 0.45 µm filter (Durapore, Carrigtwohill, Ireland). Thiobarbituric acid (80mM) was mixed 1:1 with each sample prior to incubation at 80°C for 40 min. The spectrophotometric absorbance was read in triplicate at 535nm on an EpsonTM (Suwa, Nagano Prefecture, Japan) microplate reader. A standard curve was created with tetraethoxypropane (TEP) to calculate TBARS. Chemicals were sourced from Sigma Aldrich (St. Louis, Mo. U.S.A).

4.2.5 Color:

A Minolta CR-300 Chroma Meter was calibrated through polyethylene (SaranTM,

Racine, WI., U.S.A.) wrap before use for product assessment. The surface color of the

85 three patties selected for color analysis (L*a*b*) was recorded in triplicate through polyethylene wrap.

4.2.6 Statistical analysis:

Response variables (TBARS, a*) were tested with an analysis of variance

(ANOVA) to determine significant effects of storage time, temperature and packaging type. Tuckey honest significant difference (HSD) was used to compare differences within discrete variables. After significance determination non-linear regression modeling was utilized to create a more specific prediction equation. Empirical model fitting will be described in detail as part of the results section. The study was replicated three times

(n=3); storage cabinets were the unit of replication, details on this execution are described in section 2.3. Analysis for the ANOVA and non-linear regressions were conducted with the fit model and non-linear regression tools, respectively, in JMP®Pro 13.1.0 (© 2016

SAS Institute Inc.).

4.3 Results and discussion

4.3.1 Influences of packaging on quality attributes

Two of the barriers were vacuum sealed with a known oxygen permeability film

(OTR<0.1, OTR <0.05) while the third barrier was simply an overwrap film (overwrap) with no vacuum. Figure 7 shows the TBARS collected over 12-month time points for the three barriers at the -15°C storage condition. At -20°C results within any month show only minor variation among barriers. The -15°C and -10°C had similar variance between 86 barrier type but the -10°C showed more month-month variability. When looking at each month individually the obvious trends from barrier type are not readily seen. The TBARS values for OTR <0.05 and OTR <0.1 barriers show visible separation between month 5 and month 8 where standard error bars do not overlap. During this time the overwrap barrier falls between or below the vacuum packages consistently. Values collected over the last three months for all three barriers show no noticeable differences. The extension of shelf life was not achieved as the result of a lower oxygen permeability package. This conclusion will be confirmed with the ANOVA statistical analysis.

The expected trends from a varied oxygen permeability in the packaging for a raw beef product is that TBARS would be slowed and depressed do to the restricted oxygen permeable barrier. Based on the results discussed this is not the case throughout the course of the yearlong study. Current literature has not produced results for ground beef packed in large units stored under variable barriers and temperatures through the entire shelf-life. Bak, Andersen, Andersen, & Bertelsen, (1999) shows that in samples of approximately 800 grams of whole shrimp the implementation of a modified atmosphere packaging (N2 flush) will slow and reduce the ultimate level of TBARS observed in the frozen samples over a year . It is difficult to compare this result to the present study were

2.4 kg of raw product were packaged together. Park et al., (2007) has shown non- significant lipid oxidation results between whole pork muscles stored frozen under either aerobic or anaerobic conditions. This result did not define the expectation due to the methodology used in the study. The samples were slaughtered the day prior to sample collection and frozen storage (-10°C) which explains the lack of differences shown in the

TBARS and free fatty acid analysis (FFA) (Labuza and Dugan 1971). Both analytics were also unable to distinguish differences throughout the 120 days of storage. Peroxide value which would capture the early signs of lipid oxidation and did change during storage showed numerical differences between packaging but were ultimately too variable to produce significance. This studies inability to show significant change over time suggests higher resolution s need for analysis of raw meat products.

Few studies have attempted to study lipid oxidation under frozen storage and even fewer have studied the oxidation reaction through the termination phase in cold temperatures. Studies of ground beef at refrigerated temperatures where the reactions are not slowed due to low temperatures and limited mobility suggest oxygen availability would have an effect on shelf-life. Kerth & Rowe, (2016) showed reduced lipid oxidation in ground beef packaged with low oxygen and carbon dioxide as opposed to high oxygen or just an overwrap while stored at 2°C. In other meat systems, such as pork sausage stored refrigerated, researchers have shown TBARS values were dependent on the oxygen content which ranged from 0% to 80% in modified atmosphere packaging

(Martínez, Djenane, Cilla, Beltran, & Roncales, 2006). Also, in a controlled model system a linear correlation has been established between TBARS formation and oxygen consumption during the oxidation of polyunsaturated fats (Dahle, Hill, Holman, 1962).

Obviously, lipid oxidation is very complex reaction that is dependent on more than just the oxygen available. Many of these factors are not present during storage at room temperature or under refrigeration.

The kinetics of the oxidation reactions under frozen conditions are not well known. Properties of frozen meat systems such as the unfrozen water fraction, the glassy state and, oxygen solubility all likely play a role in the progression and formation of

TBARS. It is likely that the unfrozen water present in the frozen system is supersaturated with gasses such as oxygen and a quantity of the purged gasses then become trapped in the forming ice (Craig, et al., 1992). However, as the solute concentrations increase during freezing the solubility of oxygen will tend to decrease (Thompson and Fennema

1971). This would somewhat normalize the amount of oxygen available for sub-freezing reactions to proceed with. It is unlikely any vacuum sealing systems would utilize a vacuum powerful enough to remove sufficient oxygen before freezing to mitigate this effect. This is especially important in a ground product that was subjected to atmospheric oxygen for prolonged period of time during mixing. The beef was red in color prior to packaging suggesting a oxygenation of the muscle (Mancini and Hunt 2005). This is an important distinction from the results observed from Bak et al., (1999) where whole shrimp were studied.

The a* values recorded from -15°C storage are presented in Figure 8. Within a particular month there is no instance where one barrier falls outside the standard error of another barrier’s a* value. The barrier trends are reproduced in the -20°C and -10°C conditions further indicating the packaging did not affect the redness of the samples.

Martínez, et al., (2006) showed significant differences in metmyoglobin concentrations and a* surface values for five concentrations of oxygen between 0% and 80%. If the

89 present study had effectively modified the atmosphere for the samples differences in a* would have be expected.

A decrease in package permeability did not effectively extend the shelf-life of the ground beef at any storage temperature based on the studied parameters. The use of vacuum packaging was not superior in maintaining these quality attributes. Eating experience involved more factors than those studied here in ground beef such as flavor perception and texture which may or may not be affected by vacuum seals. The focus of packaging in this study was on how it kept oxygen out, but the packages also may serve to trap moisture and flavor volatiles in. The extent of mass transfer during storage and the effect or sensory perception requires further study.

4.3.2 ANOVA effects analysis

This study had two primary focuses: to elucidate differences between the three packaging types and to describe the effect temperature and time has on changes in lipid oxidation and color during storage. The model chosen for ANOVA (α=0.05) analysis was created using a top down approach where non-significant main effects were maintained while non-significant interactions were removed. The main effects included a continuous time variable in weeks (0 to 50), three discrete temperature variable (-10°C, -15°C, and -

20°C), and three discrete packaging variables described earlier. The interaction terms which remain include time by time and time by temperatures.

The results outlined in Table 5show that both TBARS and a* change with time and temperature. The effect of the storage unit was tested as a covariate, found to be non- 90 significant, and then excluded in further analyses. The significant time by time interaction suggests a non-proportional change for the output variables (TBARS, a*) as time progressed; a non-linear relationship exists. The significant interaction between time and temperature suggests the differences in the rates of change among the three storage temperatures will not follow a linear or proportional trend either. Both interaction terms will be considered and incorporated into later prediction models. The results show packaging type did not have a significant effect for either TBARS or Color. This non- significant packaging effect was very small compared to time and temperature and similar to the effect of the storage cabinet.

The ANOVA is an important first step in the analysis of this data and provided expectations for the creation of prediction models. While various storage times and temperatures did significantly affect the quality of the product, due to the complex interactions with time the linear ANOVA model is not a predictive tool. However, the non-significant results from this analysis should be considered reliable. The analysis provides evidence that oxygen permeability did not produce a difference in lipid oxidation or color quality. Based on this result packaging variables were reassigned as additional replications providing a robust design for the analysis of temperature on the quality attributes. The full data set with packaging types averaged as additional replicates

(n=9) are presented in Figure 5 & Figure 6. The ANOVA was reanalyzed without the packaging as a main effect and showed a significant p-value for the remaining terms. A

Tuckey HSD analysis was conducted between the three temperature terms on both

91 response variables. Each temperature condition is statistically different from the others for TBARS and a* based on the Tuckey analysis.

4.3.3 Influence of temperature on changes in lipid oxidation and color during

storage

The results presented in

Figure 9 & Figure 11 show similar yet inverse trends but also important differences between a* and TBARS change. The clear separation between temperature conditions has allowed for in depth analysis and predictive modeling of both TBARS and a* change over time. For both metrics the time by temperature interaction was significant which is visible in these figures. The -10°C conditions degraded more rapidly than would be expected given the relation of the -15° &-20°C rates. This seemingly exponential relationship between reaction rate and temperature indicate a need for addition temperature condition for future experiments. The results are consistent with Chen et al.,

(1988) who studied discoloration and TBARS formation in frozen ground beef at various temperatures. In their study the warm storage temperatures changed rapidly early and the coldest temperatures showed little change overall and were grouped close together for both TBARS and discoloration. The apparent minimum in color scores observed during storage at -10°C are consistent with Chen et al., (1988). Also, the induction period in

TBARS where growth is slow initially can be seen in both studied; this phenomena is explained by Labuza and Dugan, (1971). This period is shorter in the present work with

92 the difference attributed longer supply chains before grinding involved in the present study. Chen et al., (1988) was not able to statistically distinguish rates of change in the three coldest storage temperatures: -15°C, -18°C, & -22°C, while the present study had success finding differences between -10, -15°C & -20°C. Bhattacharya and Hanna,

(1989) & Holman, Coombs, Morris, & Bailes,( 2018) were also unable to find statistical differences in TBARS between meat stored at various frozen temperatures. The current study used ground samples from the same batch, three storage units per temperatures, and

5°C separation between storage conditions. These experimental conditions allowed for confidence in the statistical and predictive modeling of lipid oxidation and color degradation reactions which were monitored.

4.3.4 TBARS modeling: theoretical consideration

Labuza and Dugan, (1971) provide a detailed review of lipid oxidation kinetics and explain the three basic phases of the reaction: initiation, propagation, and termination. Each phase in lipid oxidation involves many reactions and intermediates all with unique kinetic properties. The initiation phase, where free radicals and reactive oxygen species (ROS) form, is primarily the result of cellular metabolism after the death of the animal (Morrissey et al. 1998). The ROS and polyunsaturated fat content should be mostly constant for all samples test. Thomsen, Lauridsen, Skibsted, & Tisbo (2005) suggest the moisture of the sample, which would be dependent on frozen storage temperature, would not affect the rates of initiation as it would be isolated to the lipid fractions. A small difference was observed in month 1 where the -10° condition (Fig. 3)

93 shows the lowest TBARS value. The slight increase in the colder storage temperatures may be explained by the increased solid nature of the lipid matrix trapping reactive species close to the fatty acid tails (Labuza and Dugan, 1971). As time progresses water soluble reactive products of alkoxyl radical β-scission break from the fatty acid tails, accumulate, and allow the oxidation reaction to spread throughout the system (Dahle et al. 1962; Buettner 1993). These radical species will also react with each other beginning the termination phase. Propagation will proceed until either oxygen is quenched or all accessible PUFA have been oxidized. The accessibility of the PUFA may be determined by the unfrozen water content of a sample stored at various frozen temperatures. With less mobility the short chain products of β-scission may find themselves in a more confined space with only each other to react with. The hypothesis is that that under frozen conditions with initiation and fat content constant the rate of propagation ultimately determines the amount of TBARS formed (ymax). This rate is dependent on temperature and more importantly water mobility. As the temperature increases the oxidative products formed have more energy and room to expand effecting a higher percentage of the available unsaturated lipids and oxidation intermediates. Furthermore. At lower temperatures with less mobility the existing free radicals will be in closer contact to each other allowing the termination reactions to take place before propagation can reach its maximum potential.

4.3.5 TBARS modeling: selection and analysis

Based on the results from frozen ground beef shown by Chen et al., (1988) and the analysis on the current results the use of a sigmoidal model to describe the TBARS vs. time relationship seems appropriate. This relationship has been used for TBARS modeling of various foods stored at room temperature at various pH conditions. The report utilizes the empirical logistic equation and points out the rate of growth is tied to the propagation phase while the upper asymptote is thought to be the point where termination reactions take over in the lipid oxidation scheme (Özilgen & Özilgen, 1990).

The induction period, referenced earlier, for the reactions are not present in Özilgen &

Özilgen's (1990) model. The induction period represents the initiation and the early stages of the propagation phase of lipid oxidation and is apparent in the present results

(Labuza and Dugan, 1971). An alternative sigmoidal relationship, a modified Gompertz model, was fit to the experimental TBARS data separated by temperature. The sigmoidal relationship is often used for microbial growth modeling (Belda-Galbis et al. 2014;

Hossain, Follett, Dang, 2016).

−푒−푘(푡−푡푚) 푦 = 푦푚푎푥 ∗ 푒 + 푦0 Eq. (1)

The modified Gompertz equation contains three parameter estimates with y0 being a constant as the average value experimentally determined from the time zero testing. The maximum TBARS (ymax) approached by a given condition may be referred to as the upper asymptote. The rate (k) is the maximum rate through the exponential phase of the curve. The inflection point (tm) determines the curvature of the model and is describe

푦 mathematically as the time when equals e-1(Phinney, Goode, Fryer, Heldman, & 푦푚푎푥

Bakalis, 2017).

The Gompertz equation (R2=0.92), behaves similarly to the logistic model and was found to have a better statistical fit. The regressions to the modified Gompertz equation for each temperature condition are shown in

Figure 9. Overall a good fit exists, however, the three time points from month’s 7- 9 show values consistently greater than the predicted ymax. The last two time points show a drop in TBARS driving the predicted ymax down to the reported level. Other reports of TBARS used on frozen meat have also observed declines after a storage (Igene et al. 1985). Plot D in Figure 9 provides a visualization of the time by temperature interaction discussed earlier. The mechanism of this difference has not been confirmed but physical differences in the state of water are likely the cause. A glass transition (Tg) temperature in beef of -

13°C has been reported suggesting a dramatic decrease in the mobility water in samples stored in conditions colder. (Brake and Fennema, 1999 & Akköse and Aktaş, 2008).

The Gompertz model was statically analyzed and the results are summarized in.

The 95% confidence interval for each of the three parameters estimates included in equation 1 are reported. The root mean square error (RMSE) for models at each temperature condition are reported as well. The RMSE is low for each curve and the R2 for the model including all three temperatures is high indicating sufficient fit. The confidence intervals can be used to determine significant differences among parameters from different temperature conditions (Phinney et al., 2017). Differences in k & tm,exist between -10°C and the colder conditions (-15°C & -20°C), but not between the colder conditions. None of the confidence intervals for the ymax parameter overlap indicating a distinct end point for TBARS was a function of temperature. Chen et al., (1988) showed

96 different end points between the warm (-5°C & -10°C) and the cold conditions (-15° -

18°, & -22°) however after the seven months of this study the TBARS still seem to be trending upward. The additional 4 months of this study allowed each temperature group to reach a well-defined asymptote.

4.3.6 TBARS modeling: time and temperature prediction

The finding that ymax is different for different storage temperatures, while interesting, creates challenges for temperature predictions. Arrhenius kinetics were attempted on the rate parameters (P), but a poor relationship was established between ln(P) and 1/K. This approach is not appropriate with each of the three temperatures ending at different points. The goal of this study was to produce a model that shows the effect small incremental differences in frozen storage temperatures had on lipid oxidation. With the model selection and this goal in mind each parameter was fit as a function of temperature. The data was separated into the replications and averaged. A polynomial fit for the natural log of the parameter, ln(P) was fit versus the inverse of the storage temperature in 1/K. The order (1st or 2nd) of the relationship was selected with discretion primarily by minimizing the R2. Coefficients for the selected relationship is presented in Table 7.

Figure 10 presents the relationship to predict parameters at 6 temperatures between -10°C and -20°. These curves visualize the effect temperature has on a very complex set of reactions under frozen conditions at sub-zero temperatures. This depiction shows quality retentions from slight increases in temperature may be feasible, while highlighting the risks of thermal abuse during frozen storage. 97

4.3.7 Redness (a*) modeling: regression and analysis

Redness (a*) values were fit to a multi-parameter non-linear equation using time

(weeks) as the independent-variable (t). The following 3-parameter exponential model predicted different final redness values for each temperature condition.

(푘푡) 푦 = 푦푚푖푛 + 푏푒 Eq. (3)

Where y is the response variable (a*), ymin, is the predicted minimum a* value approached

(asymptote), b is the scale, and k is the rate. Figure 11 shows the -10°C condition has a clear asymptote that is observed early in the storage period. The above model predicts a ymin for the -15° & -20°C conditions close to the value observed in last two time points.

However, the raw data from the -15° & -20°C conditions does not show convincing asymptotes. Instead, a steady loss of redness over the 12 time points is observed. It appears that redness loss will continue under the colder storage conditions, but to what extent is unknown. The selection of the 3-parameter exponential equation for a* fit provides utility for further transformations to a 1st order model. This model achieved adequate fit, statistically, that made it a reasonable choice for analysis. With these factors in mind, the following transformations were conducted to isolate the rate of the reaction in a one parameter model.

Transformation into a single parameter model will allow for a clear study of the effect of temperature on the color loss. To accomplish this certain assumption were made for further modeling. First, the initial color value (y0) is assumed constant and the value was selected by averaging time zero a* scores. Next the ymin value was fixed at the lower asymptote value of the -10°C condition from the initial modeling of the data. These two 98 assumptions then fixed the scale value due to the know relationship where the differences of y0 and the ymin equal the scale (b=y0-ymin) value. Regression to this new model projected lines for the colder conditions which converged with the – 10°C ymin well past the range of the experimental conditions. This manipulation was done to produce a one parameter model isolating the rate for Arrhenius kinetics. Earlier experiments by Ávila and Silva, (1999) Ahmed, Shivhare, & Ramaswamy (2002) used the resulting equation to model temperature dependence of peach pure and chili color loss, respectively:

푦−푦 푚푖푛 = 푒(−푘푡) Eq. (4) 푦0−푦푚푖푛

Equation 3 and equation 4 are equivalent, showing how first order rate constants were isolated. Figure 7 contains the raw data seen in Figure 11regressed through the new exponential model that is effectively equation 4. The fit is very good for the -20°C conditions with minor single-point variations from the regressions of the warmer temperatures; the model R2 is 0.93 overall. Table 8outlines the statistical fit of the rate parameter for each condition as well as the RMSE. The RMSE is higher in the a* model than the TBARS models but the fit appears better. This is due to the use of a one parameter model. The confidence intervals on the rate parameter are extremely small and suggests each rate of decline is in one temperature is statically different from the other two temperatures. The transformations and additional regressions of the a*data were to produce manageable data for temperature dependence modeling. The statistics concerning the rate at each temperature are clean and statically different which produced confidence in further temperature modeling.

4.3.8 Redness (a*) modeling: time and temperature prediction

The activation energy (Ea) for a* change was calculated using the Arrhenius

(1889) relationship.

−퐸 1 푙푛(푘) = 푎 ( ) ln(퐴) Eq. (5) 푅 푇

The linearized relationship shows the natural log of rate of a reaction (k) as a function of the reciprocal of the process temperature in kelvin (T). Where R is the international gas constant and A is a pre-exponential constant with its natural log representing the intercept. The activation energy was calculated by fitting a linear regression through the reaction rates measured from each experimental replication versus the storage temperature. Figure 12shows this regression and the activation energy calculated was

122.3kj/mol. Chen et al., (1988) also produced an activation energy for discoloration in ground beef and reported a value of 82.5kj/mol. The variation in these numbers is likely attributed to the use zero order modeling instead of first order modeling as used in the present study. Chen et al., (1998) appear to ignore the extremely exponential curve in their warmest temperature condition focusing on the linear curves in the coldest temperature. The zero order model under predicts the rate constant at the warmest temperature thus reducing the apparent temperature dependence of the color loss phenomena.

The Ea was used to create Figure 13 which depicts a year of storage at 6 temperatures between -10 and -20 °C. The predicted plot for a* shows very similar trends seen in the TBARS plot. The coldest temperatures show very little separation indicating a

100 potential for slightly increased storage temperature. The results also indicate the risk of thermal abuse showing the much higher rates of color loss seen from a 6-10°C increase in temperature. This is a direct result from the first order modeling used to create the model.

4.4 Conclusions:

The evolution of lipid oxidation and redness degradation were studied under multiple packaging types and at multiple frozen storage temperatures in ground beef.

Decreased oxygen permeability or even vacuum packaging did not appreciably extend ground beef shelf-life based on TBARS and a* attributes. Oxygenation during pre- process handling is likely the culprit for these unexpected results. Meat quality and shelf- life are too complex to rely on just TBARS and a*, so consumer acceptance testing would be a beneficial next step to determine the efficacy of modified permeability packaging in frozen ground meat products.

Interesting results showing a clear temperature dependent maxima or minima in

TBARS and possibly a*, respectively. While the a* loss seems to still be in progress after

12 months at cold storage temperatures the TBARS values show a more defined endpoint. The results in TBARS are theoretically conceivable thus required deviations from traditional temperature modeling. Redness, after some manipulation, was fit to a first order Arrhenius model. Both metrics showed similar trends with temperature and highlight the potential risk of large fluctuations in storage temperature. The rate of lipid oxidation seemed to be more effected by the warm storage condition than redness. This observation may be attributed to the glass transition temperature and how that effects the 101 oxidation of lipids and proteins differently. This study effectively described the evolution of important meat quality parameters and discriminated between storage temperature conditions. Unique challenges in frozen meat shelf-live predictions for storage temperature optimizing have been uncovered and described.

4.5 References:

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106

4.5 Tables and Figures

Table 5: Whole model (eq.1) ANOVA significance table for effects of temperature (- 10°C, -15°C, -20°C), time (weeks), and packaging type (OTR <0.05, OTR <0.1, overwrap) on beef patties stored frozen.

ANOVA effect P value

parameter TBARS1,2 a*1,3

Intercept <0.0001 <0.0001

Time (weeks) <0.0001 <0.0001

Temperature <0.0001 <0.0001

Package Type 0.1187 0.6490

Time x Time <0.0001 <0.0001

Time x <0.0001 <0.0001

Temperature

1α=0.05

2 TBARS whole model R2=0.88

3a* whole model R2=0.86

107

6 OTR<0.05 5 OTR<0.1 Overwarp 4

mg/kg 3

2 TBARS TBARS

0 0 1 2 3 4 5 6 7 8 9 10 11 Month

Figure 5: Comparison of TBARS experimental data presenting three barriers OTR <0.05 (black), OTR <0.1 (gray with dots) & overwrap (white) through 11 months of -15°C frozen storage (n=3).

108

24 22 OTR<0.05 20 OTR<0.1 18 Overwarp 16 14 12 10

a* Redness a* 8 6 4 2 0 0 1 2 3 4 5 6 7 8 9 10 11 Month

Figure 6: Comparison of redness (a*) experimental data presenting three packaging types L (black), H (gray) & O (white) through 11 months of -15°C frozen storage. (n=3)

109

5.0 -10°C a 4.5 a a -15°C a 4.0 1 a -20°C a a a 3.5 a b ab b a b 3.0 c b b b c b c 2.5 c b 2.0 a b b c b TBARS mg/kg TBARS 1.5 c 1.0 b b a b b 0.5 0.0 0 1 2 3 4 5 6 7 8 9 10 11 time (month)

Figure 7: TBARS experimental data presenting cumulative averages of all packaging types and replications including three storage conditions -10°C (black), -15°C (gray with dots), and -20°C (white) through 11 months of storage (n=9). Different letter super scripts indicate statistical differences within months.

110

25 -10 -15 -20 20 c b b c c b b b b b b c c b 15 b b a b b

a* b a a a a 10 a a 1 a a a 5

0 0 1 2 3 4 5 6 7 8 9 10 11 Month Figure 8: Redness (a*) experimental data presenting cumulative average of all packaging types and replications including three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=9)

111

5 5 A. B. -15°C 4 4 -15°C model 3 3 2

2 RS TBA TBA RS TBA 1 -10°C 1 -10°C model 0 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Month Month

5 5 C. -20°C D. 4 .. -20°C model 4 3 3

2 2

TBARS TBA RS TBA 1 1 R2=0.92 0 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Month Month

Figure 9: Non-linear regression of TBARS results fit to the Gompertz equation (2). Plots

A (-10°C), B (-15°C), & C (-20°C) demonstrates replication averages fit to predicted

Gompertz model. Plot D combines the models from the three temperatures for comparison.

112

Table 6: Parameter estimates and 95% confidence intervals for the TBARS regression to Modified Gompertz (eq.2)

Temperature Gompertz (eq.2) model parameter estimates

condition: ±95%CI RMSE

ymax (TBARS) k (1/weeks) tm (week)

-10 3.3 ± 0.16 0.28 ± 0.18 7.6 ±0.87 0.50

-15 2.5 ± 0.14 0.13 ± 0.036 10.2 ± 1.13 0.31

-20 2.06 ± 0.12 0.11± 0.022 11.4 ± 1.09 0.22

113

Table 7: Table displaying the coefficients used to fit each Gompertz parameters versus

1/K to polynomial relationship Temperature

coefficients 2 ln(푃) = 훽1푥 + 훽2푥 + 훽0

1 x= 퐾푒푙푣푖푛

2 2 Modified 훽1 훽2 훽0 (intercept) R

Gompertz (eq. 2)

parameter (P):

ymax 0 -2817.5 11.869 0.91

k -8x107 667388 -1309.4 0.79

7 tm -2x10 17558 -343.9 0.89

114

5 -10°C -12°C -14°C 4 -16°C -18°C -20°

TBA# 2

0 0 10 20 30 40 50 Week

Figure 10: Interpolated prediction of TBA# over one year for selected even temperature values

115

15 a* 10

R2=0.93 5 exp model -10 exp model -15 exp model -20 -10 -15 -20 0 0 2 4 6 8 10 12 Month

Figure 11: Non-linear regression of redness (a*) to the three-parameter exponential model (eq. 4)

116

Table 8: Table showing first order exponential model (eq. 4) rate constant k for a* at 3 storage temperatures accompanied by RMSE for the fit of each temperature conditions.

Temperature (°C) Rate (1/weeks) RMSE

-10 0.134±0.013 1.02

-15 0.027±0.0019 1.49

-20 0.017±0.00093 1.27

117

T °C -5 -10 -15 -20 -25 0

-1 y = -14,713.69x + 53.75 R² = 0.91 -2

ln (k) ln -3 -4 -5 0.00373 0.00383 0.00393 0.00403 1/T

Figure 12: Arrhenius relationship for the natural log of the rate of color degradation versus temperature

118

15 a* 10

5 -10°C -12°C -14°C -16°C -18°C -20°C 0 0 10 20 30 40 50 Week

Figure 13: One-year interpolated temperature predication for a* using first order Arrhenius kinetics (eq. 4, 5)

119

Chapter 5: Conclusions

Based on the data presented in this work the complexity and challenges associated with muscle analysis is apparent. The results in chapter 3 ultimately contained too much variability for successful descriptions of the role temperature played in the evolution of quality. The ability to predict the time required to reach a specific drip loss % is a useful achievement. The mathematical methods used should be considered when the storage temperature’s effect on reaction rate is small while other parameters are influenced. The results in Chapter 4 also required unconventional temperature modeling. The ground beef is a more homogenous and uniform sample; unfortunately, the grinding process effects the water holding abilities focused on in Chapter 3. The quality parameters measured in the ground beef showed clear exponential or sigmoidal relationship with time and like drip loss% from chapter 3 all appeared to have a final quality level dependent on temperature. These end points are unexpected and resulted in the manipulations used to describe the effect of temperature (Ch.3.3.7, Appendix C). The end points in drip loss%

(Ch. 3) and TBARS (Ch. 4) both become apparent after month 4, leading to hypotheses for connections between the two processes.

The unfrozen water fraction and the rate of ice recrystallization both correlate with the asymptote values from for drip loss% and TBARS (Martino and Zaritzky 1988;

Boonsupthip and Heldman 2007). Speculatively, the percent unfreezable water at a given

120 temperature may define a surface area providing contact with water and oxygen to the surrounding muscle components. During isothermal storage these small aqueous pockets shift and grow due to the recrystallization of the ice fraction. The water and oxygen facilitate lipid and protein oxidation until a limiting factor, polyunsaturated fatty acids or oxygen, are exhausted. Protein oxidation is accelerated by lipid oxidation, thus not fully describing the mechanism for the end point of drip loss% or color.

The present research attempted to produce a useful tool for processors that may not require long-term storage. Warmer storage temperatures are expected to shorten a products shelf life. With reliable predictions and accurate storage limits products, not requiring long term storage, could be effectively preserved at warmer set points while decreasing energy consumption. The creation of a mathematical model which describes the evolution of quality attribute evolution over time is the basis for such a tool. Linear and non-linear regression analysis will be essential statistical methods to predict real time quality from experimental data. The effect temperature has on food quality attributes has been modeled according to Arrhenius (1889) in previous studies (Chen et al. 1988; Ávila and Silva 1999; Frelka et al. 2017) but often with reservation. To best accommodate temperature modeling statistical estimation of a rate constants in a one parameter model will be the first priority when describing the effect of time. Inevitably, novel approaches are required for testing multi-phase reactions for long term periods. Multi-parameter non- linear models have been used and the mathematical and statistical considerations taken have been discussed in the results as well as in appendix.

121

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Appendix A.; Additions to Chapter 3: The effect of

variable frozen storage temperatures on chicken quality

and water holding attributes: Methodology flow chart,

moisture balance, and full results

A.1 Introduction:

The experiments and results described in Ch. 3 “thenerkjg” was a subset of the analysis conducted on the samples. Due to multiple factors a portion of the analytics were not included in the manuscript presented in chapter three. Certain data sets referred to in

Ch. 3 were highly variable and did not contribute to the discussion presented. Appendix # will be a “full disclosure” of experimental design and results. An ideal moisture balance will also be presented for future analysis of drip and cook loss. The remainder of the study are being included for the sake of future work; analysis and discussion will be primarily focused on method improvement and less on interpretation.

Chapter 3 section 2.1 and 2.2 describe the sample preparation and handling which will be the same for all results presented below.

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A.2 Methodology flow chart and moisture balance:

Error! Reference source not found. outline all sample handling and methodology used in the experiment referred to in Ch. 3 section 2.1.through 2.8. Important masses are also identified in the flow chart as well as formula for drip loss, cook loss, and wet basis moisture content (WBMC). Five analytics were conducted but not included in Ch. 3 :

MRI, Cook loss, Rheology and SDSPAGE. Drip loss- b was conducted identical as described in section 2.3 but for group B

A.3 Drip Loss Mass Balance:

Mch1[mcch1] = Mch2[mcch2] +Mw1[mcw1]

Cook loss Mass Balance:

Mch2[mcch2] = Mch4[mcch4]+Mw3[mcw3]

Whole process Mass Balance

Mch1[mcch1]= Mch4[mcch4]+Mw1[mcw1] +Mw3[mcw3]

Whole Process with Brine Constant

Mch1[mcch1]+Mw2[mcw2] =Mch4[mcch4]+Mw1[mcw1]+Mw3[mcw3]

Mass of chicken breast:

• Mch1= pre-thawed

• Mch2-A, Mch2-B= after exudate is removed in group A or B

• Mch3= after tumble with brine, unknown

• Mch4= after cooking

Mass of water, brine, or exudate:

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• Mw1-A, Mw1-B = exudate after thawing group A or B

• Mw2 = brine added during tumble, unknown

• Mw3 =lost matter after cooking

A.4 Additional Methods:

A.2.1 Cook loss:

Cooking the breasts was described in Ch 3. Section 2.4. Briefly, A sues vide style cooker was created from a heating source, temperature control, insulated cooler and piping. The machine was then used to maintain a large water bath at 80°C. The chicken breasts, following brining, were vacuum seal and held for 20min in the hot water. After cooking the breasts were held at 4°C for 24 hours. The Vacuum seal bags were dried the weighed.

The bags were opened the liquid portion was removed and the breast and bag were dried of remaining water. The dried breast and bag were weighed. Cook loss percent was calculate by subtracting the bag weight from the whole package weight to find the pre- cooked mass. The subtracting the post cooked mass from this to calculate the cook loss which was divided by the calculated pre-cooked weight (Fig. 1).

A.2.2 Dynamic Rheological properties:

Rheological analysis was performed with a modified method as reported by Updike et al. (2005) and as described in Ch. 3 section 2.6 for water holding capacity determination.

A 590 µl aliquot of the supernatant was placed onto a Peltier stage on an AR-2000EX

141 rheometer (TA Instruments) with a 40 mm 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. Temperatures ramped from 40°C to 80°C at 1°C/min.

A.2.3 SDS PAGE

Supernatant obtained from the water holding analysis and used for dynamic rheological properties was used for protein identification. 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). 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 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 .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.

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A.2.4 Magnetic Resonance imaging:

Magnetic resonance (MR) images were acquired using a 3T Ingenia CX human scanner

(Philips Healthcare, Cleveland, Ohio). A transmit/receive 16 channel knee RF (radio frequency) coil was selected for the image acquisition since its imaging volume is most appropriate. The MRI measurement was done at room temperature. The MRI protocol includes a 3D T1-weighted Magnetization-Prepared Rapid Gradient-Echo (MPRAGE) sequence (180 x 140 x 120 mm field-of-view, voxel size 1.0 x 1.0 x 1.0 mm, TR/TE/TI

6.2/3.0/900 ms, flip angle 5°), a proton density-weighted Turbo Spin Echo (TSE) sequence (190 x 190 x 131 mm field-of-view, voxel size 1.0 x 1.0 x 2.5 mm, TR/TE

3500/30 ms), and a multi-echo T2-weighted TSE (T2-TSE) sequence for T2 mapping

(180 x 119 x 119 mm field-of-view, voxel size 0.9 x 1.1 x 2.0 mm, TR 2462 ms, nine equally-spaced TEs from 12 to 108 ms). (Written by John Frelka & Xiangyu Yang)

Only T2 weighted images were analyzed thus far.

A.3 Brief discussion of figures:

The omitted analytics were not discussed in Ch. 3 for specific methodological, or in the case of MRI, analytical reasons. Referring to Fig.1, Mw2 was not measured for the

Group A breasts. This is the mass after the completion of the brining process of each individual breast. Without a direct weight of this mass the ability to accurately measure pick and cook loss was lost. After cooking a subtractive method similar to drip loss determination was conducted for cook loss measurement. The results conducted from this method were not reliable as the cook bags had an extra lining capturing chicken meat mass and excess moisture during the drying process. The unknown and variable pick up 143 along with the bag weight having little to accuracy led to the omitting of cook loss from any official manuscript.

0.35 -10 0.3 -15 0.25 -20 0.2

0.15 Cook Loss % 0.1

0.05

0 0 2 4 6 8 10 12 Month shows very little change in cook loss across time and temperature. There is no useful information from these results.

The MRI results do not currently show differences across time and temperature

(Fig. 4). Further analysis can be applied to the raw image files collected during the study.

The current analysis collects the pixel intensity from each pixel inside the red region of interest (ROI) (Fig. 9). A histogram with pixel intensity bins is created to count the number of pixels with the specified intensity. A normal distribution is fit to this distribution then a full-width at half-mass estimation recorded. This analysis technique does not appear to be precise enough to determine differences in frozen storage time.

Future analysis will include the T1 and proton density images also collected during the scans. The goal of the future analysis will be to normalize the signal intensities with the proton densities to isolate differences associated with water migration cause by freezing.

Figure 23 shows the expected trends in T2 distributions between the -10°C storage after

144

12 months and the frozen control breast. The distribution broadens and there is an increase in the number of pixels with higher intensities in the stored breasts.

Considering Group B samples, Rheology and SDSPAGE share a similar methodology issue. After collecting the supernatant from the water holding analysis it was saved. From each sub-replication a proportional amount of salt soluble protein extract was removed combined to for the replication sample. The first issue is the water holding method was augmented after 4 months. This is referenced in Ch. 3, but the were as an issue with the order of addition before the mixing and centrifuging. The month -=3

WHC results were highly variable and unreliable. Also, the protein content was not measure on these SSP samples. Without a normalized protein content the rheology measurements were inherently meaningless. This an unfortunately oversight.

A mass balance was created for the processing group of breasts (A). Due to errors in brine measurements discussed earlier this mass balance could not be compared to experimental data

145

A.4 Additional tables & figures:

Figure 14: Methodology flow chart including important mass (M) locations. “Ch” refers to chicken breast/meat mass “w” refers t moisture mass 146

1060 -20°C -15°C 1010 -10°C 960 910 860 810

760 BMORS BMORS (N) 710 660 610 560 0 2 4 6 8 10 12 Month

Figure 15: BMORS versus month showing three storage temperatures.

2.3

2.1

1.9

1.7

1.5

1.3 -10 % brine brine % uptake 1.1 -15 0.9 -20 0.7

0.5 0 2 4 6 8 10 12 Month

Figure 16: WHC versus time grouped by temperature 147

1400.0 1200.0 1000.0

800.0 G' 600.0

400.0 -10 200.0 -15 0.0 -20 0 2 4 6 8 10 12 Month

Figure 17: Maximum G’, solid-like storage modulus, versus month presenting three storage temperature conditions.

148

14.0

12.0

10.0

8.0

6.0 T2 FWHM T2 4.0 -20 -15 2.0 -10 0.0 0 2 4 6 8 10 12 Month

Figure 18: Full width at half mass (FWHM) of average T2 distributions versus month presented or three storage temperatures.

149

0.1

0.0 -10 Group B Drip Loss Loss % B Drip Group 0.0 -15 0.0 -20 0 2 4 6 8 10 12 Month

Figure 19: Group B Drip loss% versus month at three storage temperatures.

150

0.35 -10 0.3 -15 0.25 -20 0.2

0.15 Cook Loss % 0.1

0.05

0 0 2 4 6 8 10 12 Month

Figure 20:Cook Loss% versus month at three storage temperatures

151

1 10

Figure 21: Representative photograph of PAGE gel:

Notes: Gel Shows no noticeable difference in band density across lanes. Lanes from left

(1) to right (10): Month 5 (M5,) -15°, M7-10°C, standard M7-20°C, M7-10°C, M7-

20°C, M7-20°C standard. (lane 9 and 10 not considered).

Quantitative analysis of gels was not completed.

152

Figure 22: MRI image showing a single internal slice of chicken (dark, solid-like) breast from T2 analysis. White (liquid-like) water standard is in bottom right while red hexagon shows the region of interest (ROI). T2 measures whiteness intensity of pixels¬.

153

2000

1500 M12 -10 frozen control 1000

bin bin 500

Pixel count per intensity perintensity count Pixel 40 60 80 100 Pixel Intensity

Figure 23: Histograms showing counts of bins representing T2 pixel intensity. Two distributions are shown: the average of the breasts stored at -10°C for 12 months and the frozen control breasts.

154

Figure 24: Photograph of chicken samples showing white striping defect (left) compared with a typical healthy breast (right).

155

A.4.1Addition correlation tables from Chapter 3

Table 9: Correlations between frozen chicken attributes across 12 months of storage, not separated by storage temperature:

DL WBMC BMORS L* a* b* DL 1 -0.38351 -0.33041 -0.26291 0.23171 -0.0521 1 WBMC 1 0.1712 0.3819 -0.0435 0.0179 BMORS 1 -0.0153 0.0337 -0.1029 1 L* 1 -0.0193 0.3645 1 a* 1 -0.2502 b* 1 1statistically significant correlation R values, α=0.05

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Table 10: Correlations between frozen chicken attributes across 12 months of storage, - 15°C storage

DL WBMC WHC L* a* b* BMORS DL 1 -0.35451 -0.44061 -0.38021 0.2187 -0.1251 0.332 WBMC 1 0.118 0.45461 0.1758 -0.0435 -0.042 WHC 1 -0.1379 -0.45391 0.0469 -0.1075 L* 1 0.37541 0.1863 -0.1948 a* 1 -0.294 -0.14 b* 1 0.0469 BMORS 1 1statistically significant correlation R values, α=0.05

157

Table 11:Correlations between frozen chicken attributes across 12 months of storage, - 20°C storage

DL WBMC WHC L* a* b* BMORS DL 1 -0.2436 -0.1951 -0.0743 0.1512 -0.0675 -0.0982 WBMC 1 -0.0374 0.1885 -0.0227 0.1802 0.39081 WHC 1 0.5251 0.1737 0.46731 -0.1881 L* 1 -0.064 0.53331 0.2187 a* 1 -0.3283 -0.2196 b* 1 -0.1195 BMORS 1 1statistically significant correlation R values, α=0.05

158

Appendix B. Moisture analysis of ground beef

additional result from Ch. 4 : Effect of storage time,

temperature and package on lipid oxidation and color of

frozen ground beef patties

B.1Beef Moisture content method:

Table 1: Moisture content was determined using an alteration of AOAC method 950.46B in which samples (2.5-3.5g) were measured on to pans of known weight and recorded.

The pans were then placed in a moisture drying oven set to 110°C for 17 hours. After drying the pan were weighed. Wet basis moisture was calculated.

B.2 Wet Basis Moisture Content discussion

Wet basis moisture content collected from the studies presented in Chapter

4 are presented in Table 1. The R2 for ANOVA model of WBMC results was very low, 0.33, indicating the data is variable and low model adequacy. The

ANOVA is result aligns with the raw data, especially in the early months (0-4) of the studies like the cause of overloading the moisture oven. Significance between beef packages is seen, however the result is not enough to make any conclusions on quality retention. Further analysis shows via Tuckey HSD shows that the

159 differences came between the two vacuum seal packages. WBMC did not appear to be affected by storage temperature. For all three storage temperatures and all three bag types

Moisture content results were excluded from Chapter 4 due to possible experimental error. The above charts all show an upward trend in moisture content that according to table is not significant. Packaging type here is significant but the overall model is a poor fit thus making any conclusion very weak. The high variability in the first four months is likely the result of experimental procedures.

As time went on the number of samples completed in a day was decreased this likely allowed for a less crowded drying oven and a more complete drying. Also, the handling during thawing became more uniform due to less samples being conducted on the same day. This likely had an effect

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Appendix B Tables and Figures

Table 12: Linear ANOVA results (α=0.05) wet basis moisture of ground beef Linear ANOVA results (α=0.05) wet basis moisture of ground beef P value ANOVA effect parameter Term Beef WBMC1,4 Intercept µ <0.0001 Time βi 0.0515 Temperature τj 0.0376 Package Type τk 0.0029 Time X time (ββ)i i 0.7187 Time X Temperature (Τβ)i j 0.0029 1α=0.05 4WBMC whole model r2=0.33 5Chicken WBMC r2=0.19

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62 OTR>0.05 60

-10 Wet Basis Moisture % Moisture Basis Wet 50 -15 48 -20 46 0 1 2 3 4 5 6 7 8 9 10 11 12 Month Figure 25: Wet basis moisture content of the <0.5 OTR vacuum bags presenting three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=3)

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62 OTR<0.01 60

52 -10 50 Wet Basis Moisture % Moisture Basis Wet -15 48 -20 46 0 1 2 3 4 5 6 7 8 9 10 11 12 Month

Figure 26: Wet basis moisture content of the <0.1 OTR vacuum bags presenting three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=3)

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62 Open 60

52 -10

50 -15 Wet Basis Moisture % Moisture Basis Wet

48 -20

46 0 1 2 3 4 5 6 7 8 9 10 11 12 Month Figure 27:Wet basis moisture content of the open bags presenting three storage conditions -10°C, -15°C, and -20°C through 11 months of storage (n=3)

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

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Appendix C: Procedure for Non-isothermal predictions

for any non-linear model

C.1 Purpose:

Creating an adaptive predictive model for a quality attribute which fits best with a non- linear model as a function of time under any non-isothermal conditions not exceeding the experimental isothermal range in which the data was collected.

This should be considered a last resort when Arrhenius kinetic models fail to produce adequate fit for the experimental data. Procedure adapted from the works of (Peleg and

Chinachoti 1996; Corradini and Peleg 2005, 2006)

C.2 Procedure:

1. Establish model to be used for non-linear modeling of data. There are two main

criteria for this model:

a. The model with the best fit to experimental data was found.

b. Determination of temperature relationship for model parameters. The best

model fit from “a” may have to be reconsidered if a similar model has

parameters which are more easily modeled as a function of temperature.

The minor loss in fit of the overall model will be made up for in the

predictive ability of the parameters by temperature.

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i. Data transformations to Kelvin vs the natural log of the parameter

or 1/kelvin vs the parameter can be considered.

ii. Temperature relationship should be considered based on the

parameters of each replication regressed against temperature.

iii. A Minimum of four temperature conditions are required for proper

modeling

iv. 2nd order polynomials have been used to fit this regression, 3

temperature conditions lack proper degrees of freedom

2. With the relationship between each parameter and temperature identified

isothermal storage predictions are created. This is used as a reference for the non-

isothermal prediction moving forward.

3. Solve the model equation used for time.

4. Solve the model equation used for its derivative with respect to time.

5. Using excel, Create a column for “process time” and “thermal history”

6. Using excel, Create a column for each parameter as a function of temperature.

7. The initial concentration or quality level should be set at time 0.

8. Create a t* , dy/dt, and cumulative concentration column

a. t* - this is the time that corresponds to the new process condition’s model

parameters at the same quality level of the current process.

b. dy/dt- this is the rate of change for the new process parameters, t* is used

for time in this calculation

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c. cumulative concentration- the sum of each time points dy/dt will create a

quality level vs time curve for the given thermal history.

Considerations made for parameter estimations used in dy/dt:

1. When solving iteratively with excel as described above a difference

equation is used instead of a true differential equation. This results in the

parameter estimates used for dy/dt to be the average of the current

condition and the t-1 conditions.

a. This promotes smoother transitions during temperature fluctuations

b. If a differential equation solver is used (matlab) this is not required

as dt is much smaller.

2. Asymptote parameters require special consideration and knowledge of the

underlying reactions. The major problem with the asymptote is that the

current quality level can be greater than the current asymptote value. This

occurs when cold storage follows warm storage such as in fig 1. The

mathematical if then statement may be used:

a. if “current asymptote” < “current y”, then use “maximum

asymptote”

i. Maximum asymptote refers to the largest asymptote

observed in the experimental range

b. If “current asymptote”> “current y”, then use the largest asymptote

from previous process

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i. This is an important stipulation that provides the simplest

action for how to handle the cumulative effect of

temperature fluctuations on the asymptote parameter.

Ω- Quality level at current process time as a result of the previous thermal history. Θ- quality level equal to Ω at time, t*corresponding to the new process temperature. dy/dt- calculated based on the current process time’s (t) quality level with the new process times (t*) model parameters. This procedure allows for the use of the black dy/dt in place of the gray dy/dt

Figure 28: Non-linear, non-isothermal schematic describing the logic used for replacing the instantaneous rate of new process parameters onto a curve from the current process parameters in non-isothermal, non-linear modeling of quality progression.

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