1

Influence of Storage Temperature on Changes in Frozen Meat Quality

Thesis

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

the Graduate School of The Ohio State University

By

Jeffrey Caminiti

Graduate Program in Food Science and Technology

The Ohio State University

2018

Thesis Committee

Dennis Heldman, Advisor

Macdonald Wick, Advisor

Christopher Simons

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Copyrighted by

Jeffrey T. Caminiti

2018

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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

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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

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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

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reality. A final thanks to the livestock and the generous industry suppliers of the copious

amount of meat samples used in these studies.

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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

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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

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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

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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

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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

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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

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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

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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.

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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

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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

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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).

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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 &

8

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.

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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.

10

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

11

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.

12

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

13

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

14

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).

15

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.

16

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

17

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

18

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

19

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

20

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

21

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.

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).

22

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

23

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

24

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

25

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

26

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

27

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

28

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.

29

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

30

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.

31

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

order kinetics. Chen, et al. (1988) reported activation energies, Q10, & R2 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.

32

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

33

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).

34

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

35

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.

2.8 References:

Akköse A, Aktaş N (2008) Determination of glass transition temperature of beef and effects of

various cryoprotective agents on some chemical changes. Meat Sci 80:875–878. doi:

10.1016/j.meatsci.2008.04.006

Alvarado C, McKee S (2007) Marination to improve functional properties and safety of poultry

meat. J Appl Poult Res 16:113–120. doi: 10.1093/japr/16.1.113

Bahuaud D, Mørkøre T, Langsrud, (2008) Effects of -1.5 °C Super-chilling on quality of Atlantic

salmon (Salmo salar) pre-rigor Fillets: Cathepsin activity, muscle histology, texture and

liquid leakage. Food Chem. 111:329–339

Barden L, Decker EA (2013) Lipid oxidation in low-moisture food: A review. Crit Rev Food Sci

Nutr 56:2467–2482. doi: 10.1080/10408398.2013.848833

Barriuso B, Astiasarán I, Ansorena D (2013) A review of analytical methods measuring lipid

oxidation status in foods: A challenging task. Eur Food Res Technol 236:1–15. doi:

10.1007/s00217-012-1866-9

Bhattacharya M, Hanna MA (1989) Kinetics of drip loss, cooking loss and color degradation in

frozen ground beef during storage. J Food Eng 9:83–96. doi: 10.1016/0260-

8774(89)90007-1

36

Bianchi M, Petracci M, Sirri F, (2007) The Influence of the Season and Market Class of Broiler

Chickens on Breast Meat Quality Traits. Poult Sci 86:959

Boonsupthip W, Heldman DR (2007) Prediction of frozen food properties during freezing using

product composition. J Food Sci 72:254–263. doi: 10.1111/j.1750-3841.2007.00364.x

Boonsupthip W, Sajjaanantakul T, Heldman DR (2009) Use of average molecular weights for

product categories to predict freezing characteristics of foods. J Food Sci 74:. doi:

10.1111/j.1750-3841.2009.01309.x

Brake NC, Fennema OR (1999) Glass transition values of muscle tissue. J Food Sci 64:10–15.

doi: 10.1111/j.1365-2621.1999.tb09851.x

Bratzler LJ (1932) Measuring the tenderness of meat by means of a mechanical shear. Master Sci

Thesis

Brown WD, Dolev A (1963) Effect of Freezing on Autoxidation of Oxymyoglobin Solutions. J

Food Sci 28:211–213. doi: 10.1111/j.1365-2621.1963.tb00186.x

Buettner GR (1993) The Pecking Order of Free Radicals and Antioxidants: Lipid Peroxidation,

a-Tocopherol, and Ascorbate. Arch. Biochem. Biophys. 300:535–543

Caballero D, Antequera T, Caro A, (2018) Analysis of MRI by fractals for prediction of sensory

attributes: A case study in loin. J Food Eng 227:1–10. doi:

10.1016/j.jfoodeng.2018.02.005

Carpenter CE, Cornforth DP, Whittier D (2001) Consumer preferences for beef color and

packaging did not affect eating satisfaction. Meat Sci 57:359–363. doi: 10.1016/S0309-

1740(00)00111-X

37

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

Cavitt LC, Meullenet JF, Gandhapuneni RK, (2005a) Rigor development and meat quality of

large and small broilers and the use of Allo-Kramer shear, needle puncture, and razor

blade shear to measure texture. Poult Sci 84:113–118. doi: 10.1093/ps/84.1.113

Cavitt LC, Meullenet JF, Xiong R, Owens CM (2005b) The relationship of Razor Blade Shear,

Allo-Kramer Shear, Warner- Bratzler Shear and Sensory Tests to Changes in Tenderness

of Broiler Breast Fillets. J Muscle Foods 16:223–242

Cavitt LC, Youm GW, Meullenet JF, (2004) Prediction of Poultry Meat Tenderness Using Razor

Blade Shear, Allo-Kramer Shear, and Sarcomere Length. J Food Sci 69:SNQ11-SNQ15.

doi: 10.1111/j.1365-2621.2004.tb17879.x

Chen CS (1987) Relationship Between Water Activity and Freezing Point Depression of Food

Systems. J Food Sci 52:433–435

Chen CS (1985) Thermodynamic analysis of the freezing and thawing of foods : enthalpy and

apparent specific heat. J Food Sci 50:1158–1162

Chen H, Singh RP, Reid DS (1988) Quality changes in hamburger meat during fro storage. Int J

Refrig 12:

Choe E, Min DB (2007) Chemistry of deep-fat frying oils. J Food Sci 72:. doi: 10.1111/j.1750-

3841.2007.00352.x

38

Coombs CEO, Holman BWB, Collins D, (2018) Effects of chilled-then-frozen storage (up to 52

weeks) on an indicator of protein oxidation and indices of protein degradation in lamb M.

longissimus lumborum. Meat Sci 135:134–141. doi: 10.1016/j.meatsci.2017.09.013

Coombs CEO, Holman BWB, Friend MA, Hopkins DL (2017) Long-term red meat preservation

using chilled and frozen storage combinations: A review. Meat Sci 125:84–94. doi:

10.1016/j.meatsci.2016.11.025

Cross HR, West RL, Dutson TR (1981) Comparison of methods for measuring sarcomere length

in beef semitendinosus muscle. Meat Sci 5:261–266. doi: 10.1016/0309-1740(81)90016-

4

Csallany AS, Guan M Der, Manwaring JD, Addis PB (1984) Free malonaldehyde determination

in tissues by high-performance liquid chromatography. Anal Biochem 142:277–283. doi:

10.1016/0003-2697(84)90465-2

Deatherage FE, Hamm R (1960) Influence of Freezing and Thawing on Hydration and Charges

of the Muscle Proteins. J Food Sci 25:623–629. doi: 10.1111/j.1365-2621.1960.tb00006.x

den Hertog‐Meischke MJA, van Laack RJLM, Smulders FJM (1997) The water‐holding capacity

of fresh meat. Vet Q 19:175–181. doi: 10.1080/01652176.1997.9694767

Dolata W, Piotrowska E, Wajdzik J, Tritt-Goc J (2004) The use of the MRI technique in the

evaluation of water distribution in tumbled porcine muscle. Meat Sci 67:25–31. doi:

10.1016/j.meatsci.2003.09.002

E, Bilinski, R, E, E J and MDP (1981) Treatments Affecting the Degradation of Lipids in Frozen

Pacific Herring , Clupea harengus pallasi. Can. Inst. Food Sci. Technol. 14:123–127

39

Falch E, Anthonsen HW, Axelson DE, Aursand M (2004) Correlation between 1H NMR and

traditional methods for determining lipid oxidation of ethyl docosahexaenoate. JAOCS, J

Am Oil Chem Soc 81:1105–1110. doi: 10.1007/s11746-004-1025-1

Faustman C, Sun Q, Mancini R, Suman SP (2010) Myoglobin and lipid oxidation interactions:

Mechanistic bases and control. Meat Sci 86:86–94. doi: 10.1016/j.meatsci.2010.04.025

Frelka JC, Phinney DM, Wick MP, Heldman DR (2017) Reverse Stability Kinetics of Meat

Pigment Oxidation in Aqueous Extract from Fresh Beef. J Food Sci 00:1–5. doi:

10.1111/1750-3841.13976

George P, Stratmann CJ (1952) The oxidation of myoglobin to metmyoglobin by oxygen. I.

Biochem J 51:103–108

Gonzales-Sanguinetti S, Anon MC, Cavelo A (1985) Effect of Thawing Rate on the Exudate

Production of Frozen Beef. J Food Sci 50:697–700. doi: 10.1111/j.1365-

2621.1985.tb13775.x

Greene BE, Hsin I ‐M, Zipser MW (1971) Retardation of Oxidative Color Changes in Raw

Ground Beef. J Food Sci 36:940–942. doi: 10.1111/j.1365-2621.1971.tb15564.x

Hagyard CJ, Keiller AH, Cummings TL, Chrystall BB (1993) Frozen storage conditions and

rancid flavour development in lamb. Meat Sci 35:305–312. doi: 10.1016/0309-

1740(93)90036-H

Haurowitz F, Schwerin P, Mutahhar Y Destruction of Hemin and Hemoglobin by the action of

unsaturated Fatty Acids and Oxygen

Holman BWB, Coombs CEO, Morris S, (2018) Effect of long term chilled (up to 5 weeks) then

frozen (up to 12 months) storage at two different sub-zero holding temperatures on beef:

40

2. Lipid oxidation and fatty acid profiles. Meat Sci 136:9–15. doi:

10.1016/j.meatsci.2017.10.003

Honikel KO (1998) Reference methods for the assessment of physical characteristics of meat.

Meat Sci 49:447–457. doi: 10.1016/S0309-1740(98)00034-5

Hsieh RC, Lerew E (1977) Prediction of freezing times for foods influenced by product

properties. J Food Process Eng 1:183–197

Igene JO, Yamauchi K, Pearson AM, (1985) Evaluation of 2-Thiobarbituric Acid Reactive

Substances (TBRS) in Relation to Warmed-Over Flavor (WOF) Development in Cooked

Chicken. J Agric Food Chem 33:364–367. doi: 10.1021/jf00063a011

June C-J, Ochiai Y, Hashimoto K (1985) Effects of Freezing and Thawing on the Autoxidation

of Bluefin Tuna Myoglobin. Bull Japanese Soc Scietific Fish 51:2073–2078

Kamal-Eldin A, Min DB (2003) Lipid oxidation pathways. 316 s. doi: 10.1201/9781439822098

Kasapis S (2006) Definition and applications of the network glass transition temperature. Food

Hydrocoll 20:218–228. doi: 10.1016/j.foodhyd.2005.02.020

Kerth CR, Rowe CW (2016) Improved sensitivity for determining thiobarbituric acid reactive

substances in ground beef. Meat Sci 117:85–88. doi: 10.1016/j.meatsci.2016.02.041

Kohn and Liversedge (1944) On a New Aerobic Metabolite whose Production by Brain is

inhibited by Apomorphine, Emetinee, Ergotamine, Epinephrine and Menadione. ASPET

Journals 292–300

Kramer A, Aamlid K, Guyer RB, Rogers H (1951) New Shear-Press Predicts Quality of Canned

Limas. Food Eng 112–12,187

41

Labuza TP, Dugan LR (1971) Kinetics of lipid oxidation in foods. C R C Crit Rev Food Technol

2:355–405. doi: 10.1080/10408397109527127

Lawrie RA (1950) Some observations on factors affecting myoglobin concentrations in muscle. J

Agric Sci 40:356–366. doi: 10.1017/S0021859600046116

Lee YS, Owens CM, Meullenet JF (2008a) The meullenet-owens razor shear (mors) for

predicting poultry meat tenderness: Its applications and optimization. J Texture Stud

39:655–672. doi: 10.1111/j.1745-4603.2008.00165.x

Lee YS, Saha A, Xiong R, (2008b) Changes in broiler breast fillet tenderness, water-holding

capacity, and color attributes during long-term frozen storage. J Food Sci 73:. doi:

10.1111/j.1750-3841.2008.00734.x

León K, Mery D, Pedreschi F, León J (2006) Color measurement in L*a*b*units from RGB

digital images. Food Res Int 39:1084–1091. doi: 10.1016/j.foodres.2006.03.006

Lipton MMDPD (2014) No Title. In: Introd. to MRI, Albert Einstein Coll. Med.

https://www.youtube.com/watch?v=35gfOtjRcic&list=PLgCPiZS0zuHgsGS3-

dqi5UJqkYvquc99n. Accessed 6 Jan 2018

Love J, Pearson AM (1971) Lipid oxidation in meat and meat products, A review. J Am Oil

Chem Soc 48:547–549. doi: 10.1007/bf02544559

Mancini RA, Hunt MC (2005) Current research in meat color. Meat Sci 71:100–121. doi:

10.1016/j.meatsci.2005.03.003

Marion, W.W. Forsythe R. H (1963) Autoxidation of Turkey

Martino MN, Zaritzky NE (1988) Ice Crystal Size Modifications during Frozen Beef Storage.

53:1631–1637

42

Martino MN, Zaritzky NE (1989) Ice recrystallization in a model system and in frozen muscle

tissue. Cryobiology 26:138–148

McCaig TN (2002) Extending the use of visible/near-infrared reflectance spectrophotometers to

measure colour of food and agricultural products. Food Res Int 35:731–736. doi:

10.1016/S0963-9969(02)00068-6

Meléndez-Martínez AJ, Vicario IM, Heredia FJ (2005) Instrumental measurement of orange

juice colour: A review. J Sci Food Agric 85:894–901. doi: 10.1002/jsfa.2115

Meullenet JF, Jonville E, Grezes D, Owens CM (2004) Prediction of the texture of cooked

poultry pectoralis major muscles by near-infrared reflectance analysis of raw meat. J

Texture Stud 35:573–585. doi: 10.1111/j.1745-4603.2004.35510.x

Miller AJ, Ackerman SA, Palumbo SA (1980) Effects of Frozen Storage on Functionality of

Meat for Processing. J Food Sci 45:1466–1471. doi: 10.1111/j.1365-

2621.1980.tb07541.x

Molano R, Rodríguez PG, Caro A, Durán ML (2012) Finding the largest area rectangle of

arbitrary orientation in a closed contour. Appl Math Comput 218:9866–9874. doi:

10.1016/j.amc.2012.03.063

Molina-García AD, Otero L, Martino MN, (2004) Ice VI freezing of meat: Supercooling and

ultrastructural studies. Meat Sci 66:709–718. doi: 10.1016/j.meatsci.2003.07.003

Morris SG (1954) Fat rancidity, Recent Studies on the Mechanism of Fat Oxidation in Its

Relation to Rancidity. J Agric Food Chem 2:126–132. doi: 10.1021/jf60023a004

Morrissey P a., Sheehy PJ a., Galvin K, (1998) Lipid stability in meat and meat products. Meat

Sci 49:S73–S86. doi: 10.1016/S0309-1740(98)90039-0

43

Ngapo TM, Babare IH, Reynolds J, Mawson RF (1999) Freezing and thawing rate effects on drip

loss from samples of pork. Meat Sci 53:149–158. doi: 10.1016/S0309-1740(99)00050-9

Park B, Chen YR, Hruschka WR, (1998) Near-Infrared Reflectance Analysis for Predicting Beef

Longissimus Tenderness. J Anim Sci 76:2115–2120. doi: 10.2527/1998.7682115x

Park SY, Yoo SS, Uh JH, (2007) Evaluation of lipid oxidation and oxidative products as affected

by pork meat cut, packaging method, and storage time during frozen storage (-10°C). J

Food Sci 72:114–120. doi: 10.1111/j.1750-3841.2006.00265.x

Pham QT, Mawson RF (1997) Ch 5. Moisture Migration and Ice Recrystallization in Frozen

Foods. In: Quality in Frozen Foods. pp 67–91

Pietrasik Z, Janz JAM (2009) Influence of freezing and thawing on the hydration characteristics,

quality, and consumer acceptance of whole muscle beef injected with solutions of salt

and phosphate. Meat Sci 81:523–532. doi: 10.1016/j.meatsci.2008.10.006

Puolanne E, Halonen M (2010) Theoretical aspects of water-holding in meat. Meat Sci 86:151–

165. doi: 10.1016/j.meatsci.2010.04.038

Raharjo S, Sofos JN, Schmidt GR (1992) Improved Speed, Specificity, and Limit of

Determination of an Aqueous Acid Extraction Thiobarbituric Acid-C18Method for

Measuring Lipid Peroxidation in Beef. J Agric Food Chem 40:2182–2185. doi:

10.1021/jf00023a027

Salih AM, Smith DM, Price JF, Dawson LE (1987) Modified extraction 2-thiobarbituric acid

method for measuring lipid oxidation in poultry. Poult Sci 66:1483–1488. doi:

10.3382/ps.0661483

44

Schaich KM, Shahidi F, Zhong Y, Eskin NAM (2013) Chapter 11. Lipid Oxidation. Biochem

Foods 420–478

Scheffler TL, Gerrard DE (2007) Mechanisms controlling pork quality development: The

biochemistry controlling postmortem energy metabolism. Meat Sci 77:7–16. doi:

10.1016/j.meatsci.2007.04.024

Shaarani SM, Nott KP, Hall LD (2006) Combination of NMR and MRI quantitation of moisture

and structure changes for convection cooking of fresh chicken meat. Meat Sci 72:398–

403. doi: 10.1016/j.meatsci.2005.07.017

Shepherd RG (1948) A Specific Analytical Method for Certain Pyrimidines. Anal Chem

20:1150–1153. doi: 10.1021/ac60024a006

Sigurgisladottir S, Ingvarsdottir H, Torrissen OJ, Cardinal M (2000) Effects of freezing / thawing

on the microstructure and the texture of smoked Atlantic salmon ( Salmo salar ). Food

Res Int 33:857–865. doi: 10.1016/S0963-9969(00)00105-8

Tarladgis BG, Pearson AM, Jun LRD (1962) The Chemistry of the 2-thiobarbituric acid test for

determination of oxidative rancidity in foods. I. some important side reactions. J Sci Food

Agric 15:602–607. doi: 10.1002/jsfa.2740150904

Tarladgis BG, Watts BM, Younathan MT, Dugan L (1960) A distillation method for the

quantitative determination of malonaldehyde in rancid foods. J Am Oil Chem Soc 37:44–

48. doi: 10.1007/BF02630824

Troy DJ, Kerry JP (2010) Consumer perception and the role of science in the meat industry.

Meat Sci 86:214–226. doi: 10.1016/j.meatsci.2010.05.009

45

Updike MS, Zerby HN, Sawdy JC, (2005) Turkey breast meat functionality differences among

turkeys selected for body weight and/or breast yield. Meat Sci 71:706–712. doi:

10.1016/j.meatsci.2005.05.014

Van Laack (1999) Ch. 21 The role of proteins in water-holding capacity of meat. In: Quality

Attributes of Muscle foods. pp 309–318

Venturini AC, Contreras CJC, Sarantópoulos CIGL, Villanueva NDM (2006) The effects of

residual oxygen on the storage life of retail-ready fresh beef steaks masterpackaged under

a CO2 atmosphere. J Food Sci 71:560–566. doi: 10.1111/j.1750-3841.2006.00126.x

Vieira C, Diaz MT, Martínez B, García-Cachán MD (2009) Effect of frozen storage conditions

(temperature and length of storage) on microbiological and sensory quality of rustic

crossbred beef at different states of ageing. Meat Sci 83:398–404. doi:

10.1016/j.meatsci.2009.06.013

W.G. Jennings, W.L. Dunkley, and H.G. Reiber HGR (1954) Studies of Certain Red Pigments

Formed from 2-Thiobarbituric Acid

Wang B, Pace RD, Dessai a P, (2002) Modified Extraction Method for Determining 2-

Thiobarbituric Acid Values in Meat with Increased Specificity and Simplicity. Food

Chem Toxicolgy 67:2833–2836. doi: 10.1111/j.1365-2621.2002.tb08824.x

Watts BM (1954) Oxidative Rancidity and Discoloration in Meat. Adv Food Res 5:1–52

Witte C, Bailey E (1970) A New Extraction Method for Determining 2-Thiobarbituric Acid

Values of Pork and Beef During Storage. 2–5

Wu D, Sun DW (2013) Colour measurements by computer vision for food quality control - A

review. Trends Food Sci Technol 29:5–20. doi: 10.1016/j.tifs.2012.08.004

46

Xiong YL (2005) Role of myofibrillar proteins in water-binding in brine-enhanced meats. Food

Res Int 38:281–287. doi: 10.1016/j.foodres.2004.03.013

Younathan MT, Watts BM (1959) Relationship of Meat Pigments To Lipid Oxidation. J Food

Sci 24:728–734. doi: 10.1111/j.1365-2621.1959.tb17326.x

YOUNATHAN MT, WATTS BM (1960) Oxidation of Tissue Lipids in Cooked Pork. J Food

Sci 25:538–543. doi: 10.1111/j.1365-2621.1960.tb00365.x

Zachariah NY, Satterlee LD (1973) Efffect of light, Ph, and buffer strength on the autoxidation

of porcine, ovine and bovine myoglobins at freezing temperatures. J Food Sci 38:418–

420. doi: 10.1111/j.1365-2621.1973.tb01443.x

Zaritzky NE (1988) Ice crystal size modification during frozen Beef Storage. J Food Sci

53:1631–1637

47

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).

48

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) .

49

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.

50

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).

51

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

52

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

ΔE𝑖𝑗 = √(𝐿 ∗0− 𝐿 ∗𝑖𝑗)2 + (𝑎 ∗0− 𝑎 ∗𝑖𝑗)2 + (𝑏 ∗0− 𝑏𝑖𝑗)2 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

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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

54

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

55

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

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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

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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

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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).

𝑦 = 𝑦𝑚𝑎𝑥 ∗ 𝑒−𝑒−𝑘(𝑡−𝑡𝑚)+ 𝑦0 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

mathematically as the time when 𝑦

𝑦𝑚𝑎𝑥 equals e-1(Phinney, Goode, Fryer, Heldman, &

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

59

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(𝜃) = −𝑏𝑇 + 𝑐 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):

𝑡𝑖𝑚𝑒 (𝜃) =ln(l n(

𝑦𝑚𝑎𝑥𝑄𝐿

))

−𝑘+ 𝑡𝑚 Eq. 3

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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.

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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.

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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

63

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

64

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

65

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.

66

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

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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.

3.6 References

Allen CD, Fletcher DL, Northcutt JK, Russell SM (1998) The Relationship of Broiler

Breast Color to Meat Quality and Shelf-Life. Poult Sci 77:361–366. doi:

10.1093/ps/77.2.361

Alliance F food H and M (2009) Frozen Food Handling and Mechandising. McLean,

Virginia 22102

Añón MC, Calvelo A (1980) Freezing rate effects on the drip loss of frozen beef. Meat

Sci 4:1–14. doi: 10.1016/0309-1740(80)90018-2

AOAC I (1995) AOAC Official Method 950.46 Moisture in Meat. AOAC Off Methods

Anal

Bahuaud D, Mørkøre T, Langsrud, (2008) Effects of -1.5 °C Super-chilling on quality of

Atlantic salmon (Salmo salar) pre-rigor Fillets: Cathepsin activity, muscle

histology, texture and liquid leakage. Food Chem. 111:329–339

68

Bandman E, Rosser BWC (2000) Evolutionary significance of myosin heavy chain

heterogeneity in birds. Microsc Res Tech 50:473–491. doi: 10.1002/1097-

0029(20000915)50:6<473::AID-JEMT5>3.0.CO;2-R

Bekhit AED, Faustman C (2005) Metmyoglobin reducing activity. Meat Sci 71:407–439.

doi: 10.1016/j.meatsci.2005.04.032

Belda-Galbis CM, Pina-Pérez MC, Espinosa J, (2014) Use of the modified Gompertz

equation to assess the Stevia rebaudiana Bertoni antilisterial kinetics. Food

Microbiol 38:56–61. doi: 10.1016/j.fm.2013.08.009

Bianchi M, Petracci M, Sirri F, (2007) The Influence of the Season and Market Class of

Broiler Chickens on Breast Meat Quality Traits. Poult Sci 86:959

Boonsupthip W, Heldman DR (2007) Prediction of frozen food properties during freezing

using product composition. J Food Sci 72:254–263. doi: 10.1111/j.1750-

3841.2007.00364.x

Conchillo A, Ansorena D, Astiasarán I (2005) Use of microwave in chicken breast and

application of different storage conditions: Consequences on oxidation. Eur Food

Res Technol 221:592–596. doi: 10.1007/s00217-005-0077-z

Deatherage FE, Hamm R (1960) Influence of Freezing and Thawing on Hydration and

Charges of the Muscle Proteins. J Food Sci 25:623–629. doi: 10.1111/j.1365-

2621.1960.tb00006.x

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

69

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

Frelka JC, Phinney DM, Wick MP, Heldman DR (2017) Reverse Stability Kinetics of

Meat Pigment Oxidation in Aqueous Extract from Fresh Beef. J Food Sci 00:1–5.

doi: 10.1111/1750-3841.13976

Fu B, Labuza TP (1997) Shelf-life Testing: Procedures and Prediction Methods. In:

Ericksonn MC, Hung YC (eds) Quality in Frozen Foods. Chapman & Hall, pp

377–394

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

Gonzales-Sanguinetti S, Anon MC, Cavelo A (1985) Effect of Thawing Rate on the

Exudate Production of Frozen Beef. J Food Sci 50:697–700. doi: 10.1111/j.1365-

2621.1985.tb13775.x

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

packages of frozen peaches. Food Technol

Haurowitz F, Schwerin P, Mutahhar Y Destruction of Hemin and Hemoglobin by the

action of unsaturated Fatty Acids and Oxygen

Hossain F, Follett P, Dang Vu K, (2016) Evidence for synergistic activity of plant-

derived essential oils against fungal pathogens of food. Food Microbiol 53:24–30.

doi: 10.1016/j.fm.2015.08.006

70

Hughes JM, Oiseth SK, Purslow PP, Warner RD (2014) A structural approach to

understanding the interactions between colour, water-holding capacity and

tenderness. Meat Sci 98:520–532. doi: 10.1016/j.meatsci.2014.05.022

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

Kaale LD, Eikevik TM, Rustad T, Kolsaker K (2011) Superchilling of food: A review. J

Food Eng 107:141–146. doi: 10.1016/j.jfoodeng.2011.06.004

Kiani H, Sun DW (2011) Water crystallization and its importance to freezing of foods: A

review. Trends Food Sci Technol 22:407–426. doi: 10.1016/j.tifs.2011.04.011

Lee YS, Owens CM, Meullenet JF (2008a) The meullenet-owens razor shear (mors) for

predicting poultry meat tenderness: Its applications and optimization. J Texture

Stud 39:655–672. doi: 10.1111/j.1745-4603.2008.00165.x

Lee YS, Saha A, Xiong R, (2008b) Changes in broiler breast fillet tenderness, water-

holding capacity, and color attributes during long-term frozen storage. J Food Sci

73:. doi: 10.1111/j.1750-3841.2008.00734.x

Leygonie C, Britz TJ, Hoffman LC (2012) Impact of freezing and thawing on the quality

of meat: Review. Meat Sci 91:93–98. doi: 10.1016/j.meatsci.2012.01.013

Lopez-Bote CJ, Gray JI, Gomaa EA, Flegal CJ (1998) Effect of dietary administration of

oil extracts from rosemary and sage on lipid oxidation in broiler meat. Br Poult

Sci 39:235–240. doi: 10.1080/00071669889187

Mancini RA, Hunt MC (2005) Current research in meat color. Meat Sci 71:100–121. doi:

10.1016/j.meatsci.2005.03.003

71

Martino MN, Zaritzky NE (1988) Ice Crystal Size Modifications during Frozen Beef

Storage. 53:1631–1637

Miller AJ, Ackerman SA, Palumbo SA (1980) Effects of Frozen Storage on Functionality

of Meat for Processing. J Food Sci 45:1466–1471. doi: 10.1111/j.1365-

2621.1980.tb07541.x

National Agricultural Statistics Service (NASS) (2018) Poultry slaughter. Agric Stat

Board, United States Dep Agric. doi: 10.1136/vr.108.8.171

Owens CM, Woelfel RL, Hirschler EM, (2000) The characterization and incidence of

pale, soft, and exudative broiler meat in a commercial processing plant. Poult Sci

81:579–584. doi: 10.1093/ps/81.4.579

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

Phinney DM, Goode KR, Fryer PJ, (2017) Identification of residual nano-scale foulant

material on stainless steel using atomic force microscopy after clean in place. J

Food Eng 214:236–244. doi: 10.1016/j.jfoodeng.2017.06.019

Sigurgisladottir S, Ingvarsdottir H, Torrissen OJ, Cardinal M (2000) Effects of freezing /

thawing on the microstructure and the texture of smoked Atlantic salmon ( Salmo

salar ). Food Res Int 33:857–865. doi: 10.1016/S0963-9969(00)00105-8

Soyer A, Ozalp B, Dalmis U, Bilgin V (2010) Effects of freezing temperature and

duration of frozen storage on lipid and protein oxidation in chicken meat. Food

Chem 120:1025–1030. doi: 10.1016/j.foodchem.2009.11.042

72

Updike MS, Zerby HN, Sawdy JC, (2005) Turkey breast meat functionality differences

among turkeys selected for body weight and/or breast yield. Meat Sci 71:706–

712. doi: 10.1016/j.meatsci.2005.05.014

Van Laack (1999) Ch. 21 The role of proteins in water-holding capacity of meat. In:

Quality Attributes of Muscle foods. pp 309–318

Wang B, Pace RD, Dessai P, (2002) Modified Extraction Method for Determining 2-

Thiobarbituric Acid Values in Meat with Increased Specificity and Simplicity.

Food Chem Toxicolgy 67:2833–2836. doi: 10.1111/j.1365-2621.2002.tb08824.x

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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

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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

for temperature levels

Drip

Loss BMORS WHC (4-12)

-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

75

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

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

bars represent standard error.

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

0 2 4 6 8 10 12

Dri

p l

oss

%

Month

-20°C -20°C model

R2=0.71

77

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

0%

2%

4%

6%

8%

10%

12%

0 2 4 6 8 10 12

Dri

p l

oss

%

Month

-10°C model -15°C model -20°C model

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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

condition:

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

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

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

95% confidence interval curves presented.

1

3

5

7

9

11

13

15

17

-20 -15 -10

End o

f sh

elf

life

(θ,

month

)

Storage Temperature °C

upper 95% CL

Lower 95%

CL

6% quality

limit

80

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

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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

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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.

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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

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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

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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

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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

87

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

88

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.

89

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

90

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-

91

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

92

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

93

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)

94

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.

95

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).

𝑦 = 𝑦𝑚𝑎𝑥 ∗ 𝑒−𝑒−𝑘(𝑡−𝑡𝑚)+ 𝑦0 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

96

mathematically as the time when 𝑦

𝑦𝑚𝑎𝑥 equals e-1(Phinney, Goode, Fryer, Heldman, &

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

97

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.

98

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.

𝑦 = 𝑦𝑚𝑖𝑛 + 𝑏𝑒(𝑘𝑡) 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

99

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:

𝑦−𝑦𝑚𝑖𝑛

𝑦0−𝑦𝑚𝑖𝑛= 𝑒(−𝑘𝑡) Eq. (4)

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.

100

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

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

(1889) relationship.

𝑙𝑛(𝑘) =−𝐸𝑎

𝑅(

1

𝑇) ln(𝐴) Eq. (5)

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

101

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

102

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:

Ahmed J, Shivhare US, Ramaswamy HS (2002) for Thermal Degradation of Color in

Chilli Puree and Paste. 503:497–503. doi: 10.1006/fstl.897

Akköse A, Aktaş N (2008) Determination of glass transition temperature of beef and

effects of various cryoprotective agents on some chemical changes. Meat Sci

80:875–878. doi: 10.1016/j.meatsci.2008.04.006

Alliance F food H and M (2009) Frozen Food Handling and Mechandising. McLean,

Virginia 22102

Ávila IMLB, Silva CLM (1999) Modelling kinetics of thermal degradation of colour in

peach puree. J Food Eng 39:161–166. doi: 10.1016/S0260-8774(98)00157-5

Bak LS, Andersen AB, Andersen EM, Bertelsen G (1999) Effect of modified atmosphere

packaging on oxidative changes in frozen stored cold water shrimp (Pandalus

borealis). Food Chem 64:169–175. doi: 10.1016/S0308-8146(98)00152-6

Belda-Galbis CM, Pina-Pérez MC, Espinosa J, (2014) Use of the modified Gompertz

equation to assess the Stevia rebaudiana Bertoni antilisterial kinetics. Food

Microbiol 38:56–61. doi: 10.1016/j.fm.2013.08.009

103

Bhattacharya M, Hanna MA (1989) Kinetics of drip loss, cooking loss and color

degradation in frozen ground beef during storage. J Food Eng 9:83–96. doi:

10.1016/0260-8774(89)90007-1

Brake NC, Fennema OR (1999) Glass transition values of muscle tissue. J Food Sci

64:10–15. doi: 10.1111/j.1365-2621.1999.tb09851.x

Buettner GR (1993) The Pecking Order of Free Radicals and Antioxidants: Lipid

Peroxidation, a-Tocopherol, and Ascorbate. Arch. Biochem. Biophys. 300:535–

543

Chen H, Singh RP, Reid DS (1988) Quality changes in hamburger meat during fro

storage. Int J Refrig 12:

Coombs CEO, Holman BWB, Friend MA, Hopkins DL (2017) Long-term red meat

preservation using chilled and frozen storage combinations: A review. Meat Sci

125:84–94. doi: 10.1016/j.meatsci.2016.11.025

Craig H (1992) Oxygen supersaturation in ice-covered Antartic lakes: biological versus

physical contributions. Science (80- ) 255:318+

Dahle IX, Hill G, Holman T (1962) The Thiobarbituric Acid Polyunsaturated Reaction

and the Autoxidations Fatty Acid Methyl Esters. Arch Biochem Biophys

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

104

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

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

packages of frozen peaches. Food Technol

Holman BWB, Coombs CEO, Morris S, (2018) Effect of long term chilled (up to 5

weeks) then frozen (up to 12 months) storage at two different sub-zero holding

temperatures on beef: 2. Lipid oxidation and fatty acid profiles. Meat Sci 136:9–

15. doi: 10.1016/j.meatsci.2017.10.003

Hossain F, Follett P, Dang Vu K, (2016) Evidence for synergistic activity of plant-

derived essential oils against fungal pathogens of food. Food Microbiol 53:24–30.

doi: 10.1016/j.fm.2015.08.006

Igene JO, Yamauchi K, Pearson AM, (1985) Evaluation of 2-Thiobarbituric Acid

Reactive Substances (TBRS) in Relation to Warmed-Over Flavor (WOF)

Development in Cooked Chicken. J Agric Food Chem 33:364–367. doi:

10.1021/jf00063a011

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

Kerth CR, Rowe CW (2016) Improved sensitivity for determining thiobarbituric acid

reactive substances in ground beef. Meat Sci 117:85–88. doi:

10.1016/j.meatsci.2016.02.041

105

Labuza TP, Dugan LR (1971) Kinetics of lipid oxidation in foods. C R C Crit Rev Food

Technol 2:355–405. doi: 10.1080/10408397109527127

Mancini RA, Hunt MC (2005) Current research in meat color. Meat Sci 71:100–121. doi:

10.1016/j.meatsci.2005.03.003

Martínez L, Djenane D, Cilla I, (2006) Effect of varying oxygen concentrations on the

shelf-life of fresh pork sausages packaged in modified atmosphere. Food Chem

94:219–225. doi: 10.1016/j.foodchem.2004.11.007

Morrissey P a., Sheehy PJ a., Galvin K, (1998) Lipid stability in meat and meat products.

Meat Sci 49:S73–S86. doi: 10.1016/S0309-1740(98)90039-0

National Agricultural Statistics Service (NASS) (2018) Cold Storage. Agric Stat Board,

United States Dep Agric ISSN: 1948:

Özilgen S, Özilgen M (1990) Kinetic Model of Lipid Oxidation. J Food Sci 55:498–501.

doi: 10.1111/j.1365-2621.1990.tb06795.x

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

Park SY, Yoo SS, Uh JH, (2007) Evaluation of lipid oxidation and oxidative products as

affected by pork meat cut, packaging method, and storage time during frozen

storage (-10°C). J Food Sci 72:114–120. doi: 10.1111/j.1750-3841.2006.00265.x

Phinney DM, Goode KR, Fryer PJ, (2017) Identification of residual nano-scale foulant

material on stainless steel using atomic force microscopy after clean in place. J

Food Eng 214:236–244. doi: 10.1016/j.jfoodeng.2017.06.019

106

Thompson LU, Fennema O (1971) Effect of Freezing on Oxidation of L-Ascorbic Acid. J

Agric Food Chem 19:121–124. doi: 10.1021/jf60173a018

Thomsen MK, Lauridsen L, Skibsted LH, Risbo J (2005) Temperature effect on lactose

crystallization, maillard reactions, and lipid oxidation in whole milk powder. J

Agric Food Chem 53:7082–7090. doi: 10.1021/jf050862p

Wang B, Pace RD, Dessai a P, (2002) Modified Extraction Method for Determining 2-

Thiobarbituric Acid Values in Meat with Increased Specificity and Simplicity.

Food Chem Toxicolgy 67:2833–2836. doi: 10.1111/j.1365-2621.2002.tb08824.x

107

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

parameter

P value

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

Temperature

<0.0001 <0.0001

1α=0.05

2 TBARS whole model R2=0.88

3a* whole model R2=0.86

108

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).

0

1

2

3

4

5

6

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

TB

AR

S m

g/k

g

Month

OTR<0.05

OTR<0.1

Overwarp

109

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)

02468

1012141618202224

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

a* R

edn

ess

Month

OTR<0.05

OTR<0.1

Overwarp

110

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.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

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

TB

AR

S m

g/k

g

time (month)

-10°C

-15°C

-20°C

b b

a

a

1 a

a b b

a

a

a

a a

a a

c

b

b

b

c

b

c

b

b

b

b

b

c b

c

b

c

ab

a

111

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)

0

5

10

15

20

25

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

a*

Month

-10 -15 -20

b

a

a

1

b

a a

a a a a

a

c

b

b

b b

b

b b

c b c

b

c

b

c b

b

a

112

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.

0

1

2

3

4

5

0 2 4 6 8 10 12

TB

A R

S

Month

-10°C-10°C model

0

1

2

3

4

5

0 2 4 6 8 10 12

TB

A R

S

Month

-15°C

-15°C model

0

1

2

3

4

5

0 2 4 6 8 10 12

TB

A R

S

Month

-20°C-20°C model

0

1

2

3

4

5

0 2 4 6 8 10 12

TB

AR

S

Month

C.

.. D.

A. B.

R2=0.92

113

Table 6: Parameter estimates and 95% confidence intervals for the TBARS regression to

Modified Gompertz (eq.2)

Temperature

condition:

Gompertz (eq.2) model parameter estimates

±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

114

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

1/K to polynomial relationship

Temperature

coefficients

x=1

𝐾𝑒𝑙𝑣𝑖𝑛

ln(𝑃) = 𝛽1𝑥2 + 𝛽2𝑥 + 𝛽0

Modified

Gompertz (eq. 2)

parameter (P):

𝛽12 𝛽2 𝛽0 (intercept) R2

ymax 0 -2817.5 11.869 0.91

k -8x107 667388 -1309.4 0.79

tm -2x107 17558 -343.9 0.89

115

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

values

0

1

2

3

4

5

0 10 20 30 40 50

TB

A#

Week

-10°C -12°C -14°C

-16°C -18°C -20°

116

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

model (eq. 4)

0

5

10

15

20

25

0 2 4 6 8 10 12

a*

Month

exp model -10 exp model -15 exp model -20

-10 -15 -20

R2=0.93

117

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

118

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

versus temperature

y = -14,713.69x + 53.75

R² = 0.91

-25-20-15-10-5

-5

-4

-3

-2

-1

0

0.00373 0.00383 0.00393 0.00403

T °C ln

(k)

1/T

119

Figure 13: One-year interpolated temperature predication for a* using first order

Arrhenius kinetics (eq. 4, 5)

0

5

10

15

20

25

0 10 20 30 40 50

a*

Week

-10°C -12°C -14°C

-16°C -18°C -20°C

120

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

121

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.

122

Bibliography

Ahmed J, Shivhare US, Ramaswamy HS (2002) for Thermal Degradation of Color in

Chilli Puree and Paste. 503:497–503. doi: 10.1006/fstl.897

Akköse A, Aktaş N (2008) Determination of glass transition temperature of beef and

effects of various cryoprotective agents on some chemical changes. Meat Sci

80:875–878. doi: 10.1016/j.meatsci.2008.04.006

Allen CD, Fletcher DL, Northcutt JK, Russell SM (1998) The Relationship of Broiler

Breast Color to Meat Quality and Shelf-Life. Poult Sci 77:361–366. doi:

10.1093/ps/77.2.361

Alliance F food H and M (2009) Frozen Food Handling and Mechandising. McLean,

Virginia 22102

Alvarado C, McKee S (2007) Marination to improve functional properties and safety of

poultry meat. J Appl Poult Res 16:113–120. doi: 10.1093/japr/16.1.113

American Chemical Society (2002) Quality and Stability of Frozen Foods Time-

Temperature Tolerance Studies and Their Significance

Añón MC, Calvelo A (1980) Freezing rate effects on the drip loss of frozen beef. Meat

Sci 4:1–14. doi: 10.1016/0309-1740(80)90018-2

AOAC I (1995) AOAC Official Method 950.46 Moisture in Meat. AOAC Off Methods

Anal

Ávila IMLB, Silva CLM (1999) Modelling kinetics of thermal degradation of colour in

peach puree. J Food Eng 39:161–166. doi: 10.1016/S0260-8774(98)00157-5

123

Bahuaud D, Mørkøre T, Langsrud, (2008) Effects of -1.5 °C Super-chilling on quality of

Atlantic salmon (Salmo salar) pre-rigor Fillets: Cathepsin activity, muscle histology,

texture and liquid leakage. Food Chem. 111:329–339

Bak LS, Andersen AB, Andersen EM, Bertelsen G (1999) Effect of modified atmosphere

packaging on oxidative changes in frozen stored cold water shrimp (Pandalus

borealis). Food Chem 64:169–175. doi: 10.1016/S0308-8146(98)00152-6

Bandman E, Rosser BWC (2000) Evolutionary significance of myosin heavy chain

heterogeneity in birds. Microsc Res Tech 50:473–491. doi: 10.1002/1097-

0029(20000915)50:6<473::AID-JEMT5>3.0.CO;2-R

Barden L, Decker EA (2013) Lipid oxidation in low-moisture food: A review. Crit Rev

Food Sci Nutr 56:2467–2482. doi: 10.1080/10408398.2013.848833

Barriuso B, Astiasarán I, Ansorena D (2013) A review of analytical methods measuring

lipid oxidation status in foods: A challenging task. Eur Food Res Technol 236:1–15.

doi: 10.1007/s00217-012-1866-9

Bekhit AED, Faustman C (2005) Metmyoglobin reducing activity. Meat Sci 71:407–439.

doi: 10.1016/j.meatsci.2005.04.032

Belda-Galbis CM, Pina-Pérez MC, Espinosa J, (2014) Use of the modified Gompertz

equation to assess the Stevia rebaudiana Bertoni antilisterial kinetics. Food

Microbiol 38:56–61. doi: 10.1016/j.fm.2013.08.009

Bhattacharya M, Hanna MA (1989) Kinetics of drip loss, cooking loss and color

degradation in frozen ground beef during storage. J Food Eng 9:83–96. doi:

10.1016/0260-8774(89)90007-1

124

Bianchi M, Petracci M, Sirri F, (2007) The Influence of the Season and Market Class of

Broiler Chickens on Breast Meat Quality Traits. Poult Sci 86:959

Boonsupthip W, Heldman DR (2007) Prediction of frozen food properties during freezing

using product composition. J Food Sci 72:254–263. doi: 10.1111/j.1750-

3841.2007.00364.x

Boonsupthip W, Sajjaanantakul T, Heldman DR (2009) Use of average molecular

weights for product categories to predict freezing characteristics of foods. J Food Sci

74:. doi: 10.1111/j.1750-3841.2009.01309.x

Brake NC, Fennema OR (1999) Glass transition values of muscle tissue. J Food Sci

64:10–15. doi: 10.1111/j.1365-2621.1999.tb09851.x

Bratzler LJ (1932) Measuring the tenderness of meat by means of a mechanical shear.

Master Sci Thesis

Brown WD, Dolev A (1963) Effect of Freezing on Autoxidation of Oxymyoglobin

Solutions. J Food Sci 28:211–213. doi: 10.1111/j.1365-2621.1963.tb00186.x

Buettner GR (1993) The Pecking Order of Free Radicals and Antioxidants: Lipid

Peroxidation, a-Tocopherol, and Ascorbate. Arch. Biochem. Biophys. 300:535–543

Caballero D, Antequera T, Caro A, (2018) Analysis of MRI by fractals for prediction of

sensory attributes: A case study in loin. J Food Eng 227:1–10. doi:

10.1016/j.jfoodeng.2018.02.005

Carpenter CE, Cornforth DP, Whittier D (2001) Consumer preferences for beef color and

packaging did not affect eating satisfaction. Meat Sci 57:359–363. doi:

10.1016/S0309-1740(00)00111-X

125

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

Cavitt LC, Meullenet JF, Gandhapuneni RK, (2005a) Rigor development and meat

quality of large and small broilers and the use of Allo-Kramer shear, needle

puncture, and razor blade shear to measure texture. Poult Sci 84:113–118. doi:

10.1093/ps/84.1.113

Cavitt LC, Meullenet JF, Xiong R, Owens CM (2005b) The relationship of Razor Blade

Shear, Allo-Kramer Shear, Warner- Bratzler Shear and Sensory Tests to Changes in

Tenderness of Broiler Breast Fillets. J Muscle Foods 16:223–242

Cavitt LC, Youm GW, Meullenet JF, (2004) Prediction of Poultry Meat Tenderness

Using Razor Blade Shear, Allo-Kramer Shear, and Sarcomere Length. J Food Sci

69:SNQ11-SNQ15. doi: 10.1111/j.1365-2621.2004.tb17879.x

Chen CS (1987) Relationship Between Water Activity and Freezing Point Depression of

Food Systems. J Food Sci 52:433–435

Chen CS (1985) Thermodynamic analysis of the freezing and thawing of foods : enthalpy

and apparent specific heat. J Food Sci 50:1158–1162

Chen H, Singh RP, Reid DS (1988) Quality changes in hamburger meat during fro

storage. Int J Refrig 12:

Choe E, Min DB (2007) Chemistry of deep-fat frying oils. J Food Sci 72:. doi:

10.1111/j.1750-3841.2007.00352.x

Conchillo A, Ansorena D, Astiasarán I (2005) Use of microwave in chicken breast and

126

application of different storage conditions: Consequences on oxidation. Eur Food

Res Technol 221:592–596. doi: 10.1007/s00217-005-0077-z

Coombs CEO, Holman BWB, Collins D, (2018) Effects of chilled-then-frozen storage

(up to 52 weeks) on an indicator of protein oxidation and indices of protein

degradation in lamb M. longissimus lumborum. Meat Sci 135:134–141. doi:

10.1016/j.meatsci.2017.09.013

Coombs CEO, Holman BWB, Friend MA, Hopkins DL (2017) Long-term red meat

preservation using chilled and frozen storage combinations: A review. Meat Sci

125:84–94. doi: 10.1016/j.meatsci.2016.11.025

Corradini MG, Peleg M (2005) Estimating non-isothermal bacterial growth in foods from

isothermal experimental data. J Appl Microbiol 99:187–200. doi: 10.1111/j.1365-

2672.2005.02570.x

Corradini MG, Peleg M (2006) Linear and non-linear kinetics in the synthesis and

degradation of acrylamide in foods and model systems. Crit Rev Food Sci Nutr

46:489–517. doi: 10.1080/10408390600758280

Craig H et al. (1992) Oxygen supersaturation in ice-covered Antartic lakes: biological

versus physical contributions. Science (80- ) 255:318+

Cross HR, West RL, Dutson TR (1981) Comparison of methods for measuring sarcomere

length in beef semitendinosus muscle. Meat Sci 5:261–266. doi: 10.1016/0309-

1740(81)90016-4

Csallany AS, Guan M Der, Manwaring JD, Addis PB (1984) Free malonaldehyde

determination in tissues by high-performance liquid chromatography. Anal Biochem

127

142:277–283. doi: 10.1016/0003-2697(84)90465-2

Dahle IX, Hill G, Holman T (1962) The Thiobarbituric Acid Polyunsaturated Reaction

and the Autoxidations Fatty Acid Methyl Esters. Arch Biochem Biophys

Deatherage FE, Hamm R (1960) Influence of Freezing and Thawing on Hydration and

Charges of the Muscle Proteins. J Food Sci 25:623–629. doi: 10.1111/j.1365-

2621.1960.tb00006.x

den Hertog‐Meischke MJA, van Laack RJLM, Smulders FJM (1997) The water‐holding

capacity of fresh meat. Vet Q 19:175–181. doi: 10.1080/01652176.1997.9694767

Dolata W, Piotrowska E, Wajdzik J, Tritt-Goc J (2004) The use of the MRI technique in

the evaluation of water distribution in tumbled porcine muscle. Meat Sci 67:25–31.

doi: 10.1016/j.meatsci.2003.09.002

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

Muscle J Food Sci 31:196–201

E, Bilinski, R, E, E J and MDP (1981) Treatments Affecting the Degradation of Lipids in

Frozen Pacific Herring , Clupea harengus pallasi. Can. Inst. Food Sci. Technol.

14:123–127

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

128

Falch E, Anthonsen HW, Axelson DE, Aursand M (2004) Correlation between 1H NMR

and traditional methods for determining lipid oxidation of ethyl docosahexaenoate.

JAOCS, J Am Oil Chem Soc 81:1105–1110. doi: 10.1007/s11746-004-1025-1

Faustman C, Sun Q, Mancini R, Suman SP (2010) Myoglobin and lipid oxidation

interactions: Mechanistic bases and control. Meat Sci 86:86–94. doi:

10.1016/j.meatsci.2010.04.025

Frelka JC, Phinney DM, Wick MP, Heldman DR (2017) Reverse Stability Kinetics of

Meat Pigment Oxidation in Aqueous Extract from Fresh Beef. J Food Sci 00:1–5.

doi: 10.1111/1750-3841.13976

Fu B, Labuza TP (1997) Shelf-life Testing: Procedures and Prediction Methods. In:

Ericksonn MC, Hung YC (eds) Quality in Frozen Foods. Chapman & Hall, pp 377–

394

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

George P, Stratmann CJ (1952) The oxidation of myoglobin to metmyoglobin by oxygen.

I. Biochem J 51:103–108

Gonzales-Sanguinetti S, Anon MC, Cavelo A (1985) Effect of Thawing Rate on the

Exudate Production of Frozen Beef. J Food Sci 50:697–700. doi: 10.1111/j.1365-

2621.1985.tb13775.x

Greene BE, Hsin I ‐M, Zipser MW (1971) Retardation of Oxidative Color Changes in

Raw Ground Beef. J Food Sci 36:940–942. doi: 10.1111/j.1365-

2621.1971.tb15564.x

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

129

packages of frozen peaches. Food Technol

Hagyard CJ, Keiller AH, Cummings TL, Chrystall BB (1993) Frozen storage conditions

and rancid flavour development in lamb. Meat Sci 35:305–312. doi: 10.1016/0309-

1740(93)90036-H

Haurowitz F, Schwerin P, Mutahhar Y Destruction of Hemin and Hemoglobin by the

action of unsaturated Fatty Acids and Oxygen

Holman BWB, Coombs CEO, Morris S, (2018) Effect of long term chilled (up to 5

weeks) then frozen (up to 12 months) storage at two different sub-zero holding

temperatures on beef: 2. Lipid oxidation and fatty acid profiles. Meat Sci 136:9–15.

doi: 10.1016/j.meatsci.2017.10.003

Honikel KO (1998) Reference methods for the assessment of physical characteristics of

meat. Meat Sci 49:447–457. doi: 10.1016/S0309-1740(98)00034-5

Hossain F, Follett P, Dang Vu K, (2016) Evidence for synergistic activity of plant-

derived essential oils against fungal pathogens of food. Food Microbiol 53:24–30.

doi: 10.1016/j.fm.2015.08.006

Hsieh RC, Lerew E (1977) Prediction of freezing times for foods influenced by product

properties. J Food Process Eng 1:183–197

Hughes JM, Oiseth SK, Purslow PP, Warner RD (2014) A structural approach to

understanding the interactions between colour, water-holding capacity and

tenderness. Meat Sci 98:520–532. doi: 10.1016/j.meatsci.2014.05.022

Igene JO, Yamauchi K, Pearson AM, (1985) Evaluation of 2-Thiobarbituric Acid

Reactive Substances (TBRS) in Relation to Warmed-Over Flavor (WOF)

130

Development in Cooked Chicken. J Agric Food Chem 33:364–367. doi:

10.1021/jf00063a011

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

June C-J, Ochiai Y, Hashimoto K (1985) Effects of Freezing and Thawing on the

Autoxidation of Bluefin Tuna Myoglobin. Bull Japanese Soc Scietific Fish 51:2073–

2078

Kaale LD, Eikevik TM, Rustad T, Kolsaker K (2011) Superchilling of food: A review. J

Food Eng 107:141–146. doi: 10.1016/j.jfoodeng.2011.06.004

Kamal-Eldin A, Min DB (2003) Lipid oxidation pathways. 316 s. doi:

10.1201/9781439822098

Kasapis S (2006) Definition and applications of the network glass transition temperature.

Food Hydrocoll 20:218–228. doi: 10.1016/j.foodhyd.2005.02.020

Kerth CR, Rowe CW (2016) Improved sensitivity for determining thiobarbituric acid

reactive substances in ground beef. Meat Sci 117:85–88. doi:

10.1016/j.meatsci.2016.02.041

Kiani H, Sun DW (2011) Water crystallization and its importance to freezing of foods: A

review. Trends Food Sci Technol 22:407–426. doi: 10.1016/j.tifs.2011.04.011

Kohn and Liversedge (1944) On a New Aerobic Metabolite whose Production by Brain is

inhibited by Apomorphine, Emetinee, Ergotamine, Epinephrine and Menadione.

ASPET Journals 292–300

Kramer A, Aamlid K, Guyer RB, Rogers H (1951) New Shear-Press Predicts Quality of

131

Canned Limas. Food Eng 112–12,187

Labuza TP, Dugan LR (1971) Kinetics of lipid oxidation in foods. C R C Crit Rev Food

Technol 2:355–405. doi: 10.1080/10408397109527127

Lawrie RA (1950) Some observations on factors affecting myoglobin concentrations in

muscle. J Agric Sci 40:356–366. doi: 10.1017/S0021859600046116

Lee YS, Owens CM, Meullenet JF (2008a) The meullenet-owens razor shear (mors) for

predicting poultry meat tenderness: Its applications and optimization. J Texture Stud

39:655–672. doi: 10.1111/j.1745-4603.2008.00165.x

Lee YS, Saha A, Xiong R, (2008b) Changes in broiler breast fillet tenderness, water-

holding capacity, and color attributes during long-term frozen storage. J Food Sci

73:. doi: 10.1111/j.1750-3841.2008.00734.x

León K, Mery D, Pedreschi F, León J (2006) Color measurement in L*a*b*units from

RGB digital images. Food Res Int 39:1084–1091. doi:

10.1016/j.foodres.2006.03.006

Leygonie C, Britz TJ, Hoffman LC (2012) Impact of freezing and thawing on the quality

of meat: Review. Meat Sci 91:93–98. doi: 10.1016/j.meatsci.2012.01.013

Lipton MMDPD (2014) No Title. In: Introd. to MRI, Albert Einstein Coll. Med.

https://www.youtube.com/watch?v=35gfOtjRcic&list=PLgCPiZS0zuHgsGS3-

dqi5UJqkYvquc99n. Accessed 6 Jan 2018

Lopez-Bote CJ, Gray JI, Gomaa EA, Flegal CJ (1998) Effect of dietary administration of

oil extracts from rosemary and sage on lipid oxidation in broiler meat. Br Poult Sci

39:235–240. doi: 10.1080/00071669889187

132

Love J, Pearson AM (1971) Lipid oxidation in meat and meat products, A review. J Am

Oil Chem Soc 48:547–549. doi: 10.1007/bf02544559

Mancini RA, Hunt MC (2005) Current research in meat color. Meat Sci 71:100–121. doi:

10.1016/j.meatsci.2005.03.003

Marion, W.W. Forsythe R. H (1963) Autoxidation of Turkey

Martínez L, Djenane D, Cilla I, (2006) Effect of varying oxygen concentrations on the

shelf-life of fresh pork sausages packaged in modified atmosphere. Food Chem

94:219–225. doi: 10.1016/j.foodchem.2004.11.007

Martino MN, Zaritzky NE (1988) Ice Crystal Size Modifications during Frozen Beef

Storage. 53:1631–1637

Martino MN, Zaritzky NE (1989) Ice recrystallization in a model system and in frozen

muscle tissue. Cryobiology 26:138–148

McCaig TN (2002) Extending the use of visible/near-infrared reflectance

spectrophotometers to measure colour of food and agricultural products. Food Res

Int 35:731–736. doi: 10.1016/S0963-9969(02)00068-6

Meléndez-Martínez AJ, Vicario IM, Heredia FJ (2005) Instrumental measurement of

orange juice colour: A review. J Sci Food Agric 85:894–901. doi: 10.1002/jsfa.2115

Meullenet JF, Jonville E, Grezes D, Owens CM (2004) Prediction of the texture of

cooked poultry pectoralis major muscles by near-infrared reflectance analysis of raw

meat. J Texture Stud 35:573–585. doi: 10.1111/j.1745-4603.2004.35510.x

Miller AJ, Ackerman SA, Palumbo SA (1980) Effects of Frozen Storage on Functionality

of Meat for Processing. J Food Sci 45:1466–1471. doi: 10.1111/j.1365-

133

2621.1980.tb07541.x

Molano R, Rodríguez PG, Caro A, Durán ML (2012) Finding the largest area rectangle of

arbitrary orientation in a closed contour. Appl Math Comput 218:9866–9874. doi:

10.1016/j.amc.2012.03.063

Molina-García AD, Otero L, Martino MN, (2004) Ice VI freezing of meat: Supercooling

and ultrastructural studies. Meat Sci 66:709–718. doi:

10.1016/j.meatsci.2003.07.003

Morris SG (1954) Fat rancidity, Recent Studies on the Mechanism of Fat Oxidation in Its

Relation to Rancidity. J Agric Food Chem 2:126–132. doi: 10.1021/jf60023a004

Morrissey P a., Sheehy PJ a., Galvin K, (1998) Lipid stability in meat and meat products.

Meat Sci 49:S73–S86. doi: 10.1016/S0309-1740(98)90039-0

National Agricultural Statistics Service (NASS) (2018a) Poultry slaughter. Agric Stat

Board, United States Dep Agric. doi: 10.1136/vr.108.8.171

National Agricultural Statistics Service (NASS) (2018b) Cold Storage. Agric Stat Board,

United States Dep Agric ISSN: 1948:

Ngapo TM, Babare IH, Reynolds J, Mawson RF (1999) Freezing and thawing rate effects

on drip loss from samples of pork. Meat Sci 53:149–158. doi: 10.1016/S0309-

1740(99)00050-9

Owens CM, Woelfel RL, Hirschler EM, (2000) The characterization and incidence of

pale, soft, and exudative broiler meat in a commercial processing plant. Poult Sci

81:579–584. doi: 10.1093/ps/81.4.579

Özilgen S, Özilgen M (1990) Kinetic Model of Lipid Oxidation. J Food Sci 55:498–501.

134

doi: 10.1111/j.1365-2621.1990.tb06795.x

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

Park B, Chen YR, Hruschka WR, (1998) Near-Infrared Reflectance Analysis for

Predicting Beef Longissimus Tenderness. J Anim Sci 76:2115–2120. doi:

10.2527/1998.7682115x

Park SY, Yoo SS, Uh JH, (2007) Evaluation of lipid oxidation and oxidative products as

affected by pork meat cut, packaging method, and storage time during frozen storage

(-10°C). J Food Sci 72:114–120. doi: 10.1111/j.1750-3841.2006.00265.x

Peleg M, Chinachoti P (1996) On modeling changes in food and biosolids at and around

their glass transition temperature range. Crit Rev Food Sci Nutr 36:49–67. doi:

10.1080/10408399609527718

Pham QT, Mawson RF (1997) Ch 5. Moisture Migration and Ice Recrystallization in

Frozen Foods. In: Quality in Frozen Foods. pp 67–91

Phinney DM, Goode KR, Fryer PJ, (2017) Identification of residual nano-scale foulant

material on stainless steel using atomic force microscopy after clean in place. J Food

Eng 214:236–244. doi: 10.1016/j.jfoodeng.2017.06.019

Pietrasik Z, Janz JAM (2009) Influence of freezing and thawing on the hydration

characteristics, quality, and consumer acceptance of whole muscle beef injected with

solutions of salt and phosphate. Meat Sci 81:523–532. doi:

10.1016/j.meatsci.2008.10.006

Puolanne E, Halonen M (2010) Theoretical aspects of water-holding in meat. Meat Sci

135

86:151–165. doi: 10.1016/j.meatsci.2010.04.038

Raharjo S, Sofos JN, Schmidt GR (1992) Improved Speed, Specificity, and Limit of

Determination of an Aqueous Acid Extraction Thiobarbituric Acid-C18Method for

Measuring Lipid Peroxidation in Beef. J Agric Food Chem 40:2182–2185. doi:

10.1021/jf00023a027

Salih AM, Smith DM, Price JF, Dawson LE (1987) Modified extraction 2-thiobarbituric

acid method for measuring lipid oxidation in poultry. Poult Sci 66:1483–1488. doi:

10.3382/ps.0661483

Schaich KM, Shahidi F, Zhong Y, Eskin NAM (2013) Chapter 11. Lipid Oxidation.

Biochem Foods 420–478

Scheffler TL, Gerrard DE (2007) Mechanisms controlling pork quality development: The

biochemistry controlling postmortem energy metabolism. Meat Sci 77:7–16. doi:

10.1016/j.meatsci.2007.04.024

Shaarani SM, Nott KP, Hall LD (2006) Combination of NMR and MRI quantitation of

moisture and structure changes for convection cooking of fresh chicken meat. Meat

Sci 72:398–403. doi: 10.1016/j.meatsci.2005.07.017

Shepherd RG (1948) A Specific Analytical Method for Certain Pyrimidines. Anal Chem

20:1150–1153. doi: 10.1021/ac60024a006

Sigurgisladottir S, Ingvarsdottir H, Torrissen OJ, Cardinal M (2000) Effects of freezing /

thawing on the microstructure and the texture of smoked Atlantic salmon ( Salmo

salar ). Food Res Int 33:857–865. doi: 10.1016/S0963-9969(00)00105-8

Soyer A, Ozalp B, Dalmis U, Bilgin V (2010) Effects of freezing temperature and

136

duration of frozen storage on lipid and protein oxidation in chicken meat. Food

Chem 120:1025–1030. doi: 10.1016/j.foodchem.2009.11.042

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

5:157–163

Tarladgis BG, Pearson AM, Jun LRD (1962) The Chemistry of the 2-thiobarbituric acid

test for determination of oxidative rancidity in foods. I. some important side

reactions. J Sci Food Agric 15:602–607. doi: 10.1002/jsfa.2740150904

Tarladgis BG, Watts BM, Younathan MT, Dugan L (1960) A distillation method for the

quantitative determination of malonaldehyde in rancid foods. J Am Oil Chem Soc

37:44–48. doi: 10.1007/BF02630824

Thompson LU, Fennema O (1971) Effect of Freezing on Oxidation of L-Ascorbic Acid. J

Agric Food Chem 19:121–124. doi: 10.1021/jf60173a018

Thomsen MK, Lauridsen L, Skibsted LH, Risbo J (2005) Temperature effect on lactose

crystallization, maillard reactions, and lipid oxidation in whole milk powder. J Agric

Food Chem 53:7082–7090. doi: 10.1021/jf050862p

Troy DJ, Kerry JP (2010) Consumer perception and the role of science in the meat

industry. Meat Sci 86:214–226. doi: 10.1016/j.meatsci.2010.05.009

Updike MS, Zerby H, Utrata KL, (2006) Proteins associated with thermally induced

gelation of turkey breast meat. J Food Sci 71:. doi: 10.1111/j.1750-

3841.2006.00184.x

Updike MS, Zerby HN, Sawdy JC, (2005) Turkey breast meat functionality differences

among turkeys selected for body weight and/or breast yield. Meat Sci 71:706–712.

137

doi: 10.1016/j.meatsci.2005.05.014

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

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

Van Laack (1999) Ch. 21 The role of proteins in water-holding capacity of meat. In:

Quality Attributes of Muscle foods. pp 309–318

Venturini AC, Contreras CJC, Sarantópoulos CIGL, Villanueva NDM (2006) The effects

of residual oxygen on the storage life of retail-ready fresh beef steaks

masterpackaged under a CO2 atmosphere. J Food Sci 71:560–566. doi:

10.1111/j.1750-3841.2006.00126.x

Vieira C, Diaz MT, Martínez B, García-Cachán MD (2009) Effect of frozen storage

conditions (temperature and length of storage) on microbiological and sensory

quality of rustic crossbred beef at different states of ageing. Meat Sci 83:398–404.

doi: 10.1016/j.meatsci.2009.06.013

W.G. Jennings, W.L. Dunkley, and H.G. Reiber HGR (1954) Studies of Certain Red

Pigments Formed from 2-Thiobarbituric Acid

Wang B, Pace RD, Dessai a P, (2002) Modified Extraction Method for Determining 2-

Thiobarbituric Acid Values in Meat with Increased Specificity and Simplicity. Food

Chem Toxicolgy 67:2833–2836. doi: 10.1111/j.1365-2621.2002.tb08824.x

Watts BM (1954) Oxidative Rancidity and Discoloration in Meat. Adv Food Res 5:1–52

Witte C, Bailey E (1970) A New Extraction Method for Determining 2-Thiobarbituric

Acid Values of Pork and Beef During Storage. 2–5

Wu D, Sun DW (2013) Colour measurements by computer vision for food quality control

138

- A review. Trends Food Sci Technol 29:5–20. doi: 10.1016/j.tifs.2012.08.004

Xiong YL (2005) Role of myofibrillar proteins in water-binding in brine-enhanced meats.

Food Res Int 38:281–287. doi: 10.1016/j.foodres.2004.03.013

Younathan MT, Watts BM (1959) Relationship of Meat Pigments To Lipid Oxidation. J

Food Sci 24:728–734. doi: 10.1111/j.1365-2621.1959.tb17326.x

YOUNATHAN MT, WATTS BM (1960) Oxidation of Tissue Lipids in Cooked Pork. J

Food Sci 25:538–543. doi: 10.1111/j.1365-2621.1960.tb00365.x

Zachariah NY, Satterlee LD (1973) Efffect of light, Ph, and buffer strength on the

autoxidation of porcine, ovine and bovine myoglobins at freezing temperatures. J

Food Sci 38:418–420. doi: 10.1111/j.1365-2621.1973.tb01443.x

Zaritzky NE (1988) Ice crystal size modification during frozen Beef Storage. J Food Sci

53:1631–1637

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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

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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

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along with the bag weight having little to accuracy led to the omitting of cook loss from

any official manuscript.

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

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 2 4 6 8 10 12

Co

ok

Loss

%

Month

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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

146

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

147

Figure 15: BMORS versus month showing three storage temperatures.

Figure 16: WHC versus time grouped by temperature

560

610

660

710

760

810

860

910

960

1010

1060

0 2 4 6 8 10 12

BM

OR

S (N

)

Month

-20°C -15°C

-10°C

0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

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% b

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

storage temperature conditions.

0.0

200.0

400.0

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1000.0

1200.0

1400.0

0 2 4 6 8 10 12

G'

Month

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Figure 18: Full width at half mass (FWHM) of average T2 distributions versus month

presented or three storage temperatures.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 2 4 6 8 10 12

T2 F

WH

M

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-15

-10

150

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

0.0

0.0

0.0

0.1

0.1

0.1

0.1

0 2 4 6 8 10 12

Gro

up B

Dri

p L

oss

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Month

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-15

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

0

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0.1

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Co

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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.

1 10

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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¬.

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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.

.

0

500

1000

1500

2000

40 60 80 100Pix

el c

ou

nt

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r in

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sity

b

in

Pixel Intensity

M12 -10

frozen control

155

Figure 24: Photograph of chicken samples showing white striping defect (left) compared

with a typical healthy breast (right).

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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

WBMC 1 0.1712 0.38191 -0.0435 0.0179

BMORS 1 -0.0153 0.0337 -0.1029

L* 1 -0.0193 0.36451

a* 1 -0.25021

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

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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

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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

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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

ANOVA effect parameter Term P value

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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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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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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60

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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.

166

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

167

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

168

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.

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.

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