characterizing meat tenderness of finishing steers …
TRANSCRIPT
CHARACTERIZING MEAT TENDERNESS OF FINISHING STEERS FED FOR
DIFFERENT RATES OF GAIN
BY
HUNTER O’NEAL GALLOWAY
DISSERTATION
Submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy in Animal Sciences
in the Graduate College of the
University of Illinois at Urbana-Champaign, 2016
Urbana, Illinois
Doctoral Committee:
Professor Emeritus Floyd McKeith, Co-Chair
Assistant Professor Dustin Boler, Co-Chair
Professor Emeritus Tom Carr
Assistant Professor Tara Felix
Assistant Professor Anna Dilger
Nathan Pyatt, Ph.D., Elanco Animal Health
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Abstract
Objectives were to characterize effects of average daily gain (ADG) on Warner-Bratzler
shear force (WBSF) and characterize effects of ADG on postmortem proteolysis in steers.
Thirty-six steers (initial body weight 271 ± 24 kg) were stratified by weight into 6 pens of 6
steers each. Steers were limit fed the same diet to obtain 3 different rates of gain (1.0, 1.5, and
1.85 kg/d). Steers were individually weighed every 2 weeks during the study period. Feed
allocation was adjusted using the mean body weight of the pen, based on the NRC equation for
net energy for gain. Steers were slaughtered in 3 independent slaughter groups after 151, 161, or
167 days on feed. During each slaughter event, the 2 heaviest animals from each pen were
slaughtered. Four steers were removed from the study for having retained testicles. Temperature
decline data were collected during carcass chilling starting 2 hrs postmortem. In addition,
ultimate pH (UPH) of the longissimus muscle was collected. Individual grade and yield data
were collected. Western blot samples and WBSF steaks were collected 24 hrs postmortem from
the strip loin and randomly assigned to an aging time point (1, 7, 14, 21, 28, or 35 d). Western
blot samples and shear force steaks were vacuum packaged and stored at 4 °C until assigned
aging point, at which time western blot samples along with the corresponding shear steaks were
frozen. Samples were also obtained from the strip loin for collagen quantification and sarcomere
length determination. Sarcomere length was determined using laser diffraction. Soluble and
insoluble collagen content was quantified using acid hydrolysis. Cook loss, percentage of weight
loss during cooking, was calculated for each WBSF steak. Steer served as the experimental unit
for each variable measured. All 32 steers were ranked by ADG and fifteen steers were selected
for western blot analysis (5 steers with the greatest growth rate, 5 steers closest to the mean
growth rate, and 5 steers with the slowest growth rate) were analyzed for desmin and troponin T
degradation at 1 and 21 d postmortem aging time. As ADG increased by 1.0 kg/d, WBSF
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decreased by 1.42 kg in steaks aged for 1 d postmortem (1 d WBSF = 6.38 – (1.42*ADG, kg/d)
Adj. R2 = 0.36). However, ADG did not account for any of the variation in WBSF at other
postmortem aging points. A prediction model using a 4 variable equation accounted for 72% of
the variation in 1 d WBSF (1 d WBSF = -25.28 + (4.30 * UPH) – (0.29 * extractable lipid, %) +
(0.03 * skeletal maturity) + (0.20 * 1 d cook loss, %). Whereas, a 2 variable prediction equation
only accounted for 47% of the variation in WBSF at 21 d postmortem (21 d WBSF = 0.39 +
(0.10 * 21 d cook loss, %) + (0.09 * extractable lipid, %); Adj. R2 = 0.47). Degradation of the 37
kDa troponin T explained 27% of the variation observed in WBSF; however, degradation was
not significant at 1 d postmortem. In conclusion, ADG accounted for 36% of the variation in
WBSF at 1 d postmortem, but was not significant at other postmortem aging points. Multiple
linear regression models were able to account for the variation in WBSF at 1 and 21 d
postmortem.
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Acknowledgements
I would like to thank my co-advisors, Dr. Floyd McKeith and Dr. Dustin Boler, for
guiding me through multiple research trials and challenging me every step of the way. Thank
you for allowing me the opportunity to work in many different areas of the meat industry. I
would also like to thank Dr. Tara Felix for her nutritional and cattle management guidance
throughout my studies. I owe a huge thanks to Dr. Anna Dilger for challenging me in non-
applied areas of Meat Science. I would also like to thank Dr. Nathan Pyatt for providing
industry experience in the design of the study, and also thank Dr. Tom Carr and his family for
traveling to help me in my research.
In addition, I would like to thank all of my colleagues: Emily Arkfeld, Arica Bear, Kelsey
Bess, Ben Bohrer, Dr. Daniel Clark, Dianna Clark, Katelyn Jones-Hamlow, Ryan Herrick,
Jenifer Jones, Brianna Keever, Kellie Kroscher, Kelsey Little, Dr. Bradley Lowe, Martin
Overholt, Ben Peterson, and Dr. Marcos Tavárez, for all of their contributions along the way. I
would also like to thank Chuck Stites for his assistance throughout many research trails, and also
Gail Ellis for her assistance with sensory and laboratory analysis.
I would like to thank my wife, Leanne Galloway, for pushing me to achieve my dreams
and providing support through all of the stressful times. I would also like to thank my parents,
Jimmy and Renee Gaylord for supporting me in my career decisions. Most importantly, I would
like to thank God for giving me the strength to continue on during tough times.
Finally, I would like to thank Elanco Animal Health for making this project possible. It
has been an honor to be able to work with many great researchers from Elanco Animal Health
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Table of Contents
Chapter 1................................................................................................................... 1
Review of Literature ................................................................................................ 1
Introduction ............................................................................................................................... 1
Cattle Management ................................................................................................................... 2
Breed and Genetics .................................................................................................................. 2
Cattle Feeding Programs ......................................................................................................... 3
Energy Source.......................................................................................................................... 5
Time on Feed ........................................................................................................................... 6
Average Daily Gain ................................................................................................................. 6
Meat Characteristics ................................................................................................................. 7
Muscle Proteins ....................................................................................................................... 7
Sarcomere Length .................................................................................................................... 9
Protein Turnover .................................................................................................................... 11
Protein Degradation ............................................................................................................... 12
Rate of pH Decline ................................................................................................................ 15
Muscle Fiber Type ................................................................................................................. 16
Muscle Fiber Diameter .......................................................................................................... 17
Connective Tissue.................................................................................................................. 18
Immunoblotting ....................................................................................................................... 20
Protein Extraction and Solubilization .................................................................................... 20
Protein Separation.................................................................................................................. 21
Western Blotting .................................................................................................................... 22
Statistical Analysis................................................................................................................... 23
Statistical Power Analysis ..................................................................................................... 23
Regression Analysis .............................................................................................................. 24
Multicollinearity .................................................................................................................... 27
Model Selection ..................................................................................................................... 28
Conclusions ........................................................................................................................... 29
Literature Cited ....................................................................................................................... 30
Chapter 2.................................................................................................................38
Characterizing the effect of average daily gain and carcass parameters on
shear force in finishing steers ................................................................................38
Abstract .................................................................................................................................... 38
Introduction ............................................................................................................................. 39
Materials and Methods ........................................................................................................... 40
Experimental Design ............................................................................................................. 41
Diets ....................................................................................................................................... 41
Chill Rate and Ultimate pH ................................................................................................... 42
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Carcass Grading ..................................................................................................................... 42
Sample Collection.................................................................................................................. 43
Moisture and Lipid Determination ........................................................................................ 43
Warner-Bratzler Shear Force ................................................................................................. 43
Western Blotting .................................................................................................................... 44
Sample Selection ............................................................................................................................................ 44 Whole Muscle Sample Preparation ................................................................................................................ 44 Gel Electrophoresis ........................................................................................................................................ 45 Blotting ........................................................................................................................................................... 45
Sarcomere Length Determination .......................................................................................... 46
Collagen Content ................................................................................................................... 46
Statistical Analysis ................................................................................................................ 47
Results and Discussion ............................................................................................................ 48
Growth Performance and Carcass Characteristics ................................................................. 48
Laboratory Analysis .............................................................................................................. 49
Variable Selection and Analysis of Influential Points ........................................................... 49
Pearson Correlation Coefficients and Shear Force Prediction Equations ............................. 50
Prediction Models .................................................................................................................. 52
Proteolysis ............................................................................................................................. 53
One-way ANOVA analysis ................................................................................................... 54
Conclusion ................................................................................................................................ 56
Literature Cited ....................................................................................................................... 57
1
Chapter 1
Review of Literature
Introduction Consumers evaluate beef palatability based primarily on their perception of tenderness,
juiciness, and flavor (Voges et al., 2007). Researchers have reported the ability to classify
carcasses based on tenderness would allow producers to manage variation in products to increase
consumer satisfaction (Wheeler et al., 1999). Others reported that consumers are willing to pay
premiums of $1.10/kg for tender beef (Boleman et al., 1997). Nolan Ryan’s Guaranteed Tender
beef uses QualitySpec to measure infrared color and postmortem aging to evaluate each carcass
for tenderness (USDA-AMS, 2011). Even the United States Department of Agriculture (USDA)
is developing programs to assign USDA tenderness grades to beef. Current proposed absolute
Warner-Bratzler shear force (WBSF) thresholds for USDA-Certified Tender beef are between
3.9 and 4.4 kg; whereas, USDA-Certified Very Tender beef is required to have a WBSF less than
3.9 kg (USDA-AMS, 2012). Researchers reported postmortem proteolysis, marbling or
intramuscular fat content, connective tissue, and sarcomere length are four major factors that
influence meat tenderness (Belew et al., 2003). Many industry professionals are researching the
processes involved in postmortem tenderization because of decreases in tenderness associated
new growth technologies (Ouali, 1990; Claus et al., 2010). Researchers attribute the changes in
postmortem tenderization caused by new growth technologies to changes in calpastatin activity
(Parr et al., 1992). In implanted cattle, calpastatin activity and WBSF at postmortem aging times
less than 7 days are correlated (r ≥ 0.40; O'Connor et al., 1997). Researchers have reported poor
correlations (r = -0.10) between WBSF and total collagen content (Seideman et al., 1987).
However, neither sarcomere length, proteolysis, marbling, nor connective tissue have been able
to fully explain the variations in tenderness across a population of cattle (Light et al., 1985;
2
Bailey, 1985; Seideman et al., 1987; Smulders et al., 1990; Wulf et al., 1996; O’Connor et al.,
1997; Riley et al., 2003). Many feedlot studies have been conducted to evaluate the effects of
dietary treatments (high concentrate compared with forage based) and length of feeding on beef
tenderness (Epley et al., 1968; Tatum et al., 1980; Van Koevering et al., 1995; Mandell et al.,
1998). Other studies have been conducted to examine sarcomere length, muscle fiber area and
muscle fiber type to aid in the explanation of the variation of tenderness (Tuma et al., 1962;
Whipple et al., 1990; Crouse et al., 1991; Shackelford et al., 1994b; Grayson and Lawrence,
2013). However, the effects of average daily gain (ADG) on postmortem proteolysis affecting
WBSF of the longissimus muscle in cattle limit fed identical diets to achieve differences in ADG
have not been well studied. The objectives of this study were to characterize the effects of ADG
on WBSF and postmortem proteolysis and to develop prediction equations using measured
carcass traits to identify carcasses with a greater probability of meeting consumer expectations
for tenderness.
Cattle Management
Breed and Genetics
Two major subspecies of cattle, Bos indicus and Bos taurus, have been developed over
time to meet the demands of different climates. Bos indicus cattle evolved to live in tropical
climates; whereas, Bos taurus cattle evolved to live in more temperate climates. Globally, more
cattle exist in tropical or sub-tropical climates where greater humidity levels and temperatures
are more favorable for Bos indicus cattle (Baruselli et al., 2004). However, Bos indicus cattle
have greater challenges with tenderness as indicated by greater shear force values and are
tougher in sensory panel evaluations (Ramsey et al., 1963; Luckett et al., 1975; Koch et al.,
1982; Ferguson et al., 2000). The difference in toughness between these breeds is due to an
3
increase in calpastatin activity, calcium dependent protease inhibitor, limiting postmortem
proteolysis (Wheeler et al., 1994; Ferguson et al., 2000) in Bos indicus cattle as compared with
Bos taurus. In addition, crossbreeds of Bos indicus and Bos taurus cattle have been developed to
fit geographical or production needs. Crossbreeding of Bos indicus and Bos taurus results in
heterosis; therefore, crossbred cattle have faster rates of ADG and are less susceptible to heat
stress than Bos taurus (Frisch, 1987). In early research, Knapp and Nordskog (1946) reported
that ADG was highly heritable (heritability = 46%). In support, MacNeil et al. (1991) also
reported ADG was highly heritable. However, crossbreeding to increase ADG by introducing
Bos indicus genetics into a Bos taurus herd has resulted in less tender beef herds. Shackelford et
al. (1994a) reported 24 h postmortem calpastatin activity was highly heritable (ĥ2 = 0.65) in
cattle. Other studies have also reported calpastatin activity to be highly heritable (O'Connor et
al., 1997; Smith et al., 2007). Pringle et al. (1997) reported calpastatin activity increased linearly
(P < 0.01) as the percentage of Brahman (Bos indicus) inheritance increased in Angus x
Brahman crosses. In a comparison of British with Continental cattle, purebred Angus (British)
cattle have lower calpastatin activity as compared with purebred Charolais (Continental) cattle
(Shackelford et al., 1994a). In crossbred cattle, Riley et al. (2003) reported sire was the source of
variation in µ-calpain and calpastatin activity.
Cattle Feeding Programs
Cattle management strategies have been adapted to take advantage of the specific
resources in a given area; therefore, cattle management strategies are used to increase production
efficiency or reduce input costs. In North America, 80% of cattle are fed concentrate-based diets
during the finishing phase (Mathews and Johnson, 2013). Globally, 12% of beef is grown in an
intensive feeding system with the remaining 88% of cattle being grown using grazing systems
(FAO, 1996). When cattle are fed concentrate diets (diets with dry matter digestibility greater
4
than 69%) their intake is regulated by chemostatic means instead of physical capacity, as is the
case with cattle fed forage/roughage-based diets (diets with dry matter digestibility less than
69%; Cheeke, 2005). Chemostatic regulation of feed intake uses receptors to measure the levels
of metabolites in the blood. Researchers reported blood plasma acetate concentration is
correlated (r = 0.76, P < 0.01) with total dry matter intake (Quigley et al., 1991). After weaning,
cattle are placed into different finishing programs: such as calf-fed (cattle are fed a high
concentrate diet, diets using concentrates as the primary source of energy, after weaning until
slaughter), yearling-fed (cattle are backgrounded on a roughage-based diet for a period of time
before being fed a high concentrate diet until slaughter), or forage-fed (cattle are fed solely on
roughage until slaughter). In addition to traditional strategies, early weaning cattle, in which
cattle are weaned at approximately 150 days of age as compared to more conventional weaning
of > 200 days, can be employed as a management strategy (Myers et al., 1999). Early weaned
calves may enter the feed lot directly and be fed grain or enter a backgrounding program.
Calf-fed cattle had greater ADG and decreased WBSF, but were approximately 100 kg
lighter and 120 days younger than yearling-fed cattle when fed until the lipid content of the
longissimus muscle was predicted to be 4.5% (based on Angus x Hereford crossbreed breed data;
Dikeman et al., 1985b). When calf-fed cattle, fed on concentrate diet for 217 days, and yearling-
fed cattle, backgrounded on Bermuda grass for 124 days then fed a concentrate diet for 93 days,
were slaughtered at 16 months of age, no differences in WBSF or ADG during the study period
were reported, but calf-fed cattle were 76 kg heavier as compared with yearling-fed cattle (Harris
et al., 1997). In contrast, when calf-fed cattle, fed concentrated diet for 224 days, and yearling-
fed cattle, backgrounded on native pasture for 120 days then fed a concentrate diet for 182 days,
were fed to an equal ending live weight of approximately 505 kg, no differences in WBSF were
5
reported, but yearling fed cattle had 0.37 kg/d greater ADG compared with calf-fed cattle fed a
high concentrate diet (Harris et al., 1997). Other studies support yearling-fed cattle had greater
ADG than calf-fed cattle during the finishing phase (Dikeman et al., 1985a; Lunt and Orme,
1987; Short et al., 1999; Sainz and Vernazza Paganini, 2004; Anderson et al., 2005). The
increase in ADG during the finishing period may be caused by the quality of forage consumed
during backgrounding.
Compensatory gain is a condition in which animals, that have experienced depressed
growth rates over a long period of time due to restricted dietary intake or inadequate nutrients,
have accelerated rates of gain after the dietary restriction is resolved, or the animal is fed a
properly formulated diet (Lawrence et al., 2012). Cattle undergoing compensatory gain had
increased ADG and feed efficiency when compared with cattle provided ad libitum access to
feed (ADG of 1.56 compared with 1.18 kg/d and feed efficiency of 4.5 to 5.6 DM/kg gain,
respectively; Fox et al., 1972).
Energy Source
In addition to rate of gain, dietary energy sources can affect tenderness. In North
America, cattle are finished predominantly grain-based diets (Vasconcelos and Galyean, 2007).
Forage-based finishing systems are used in other countries where pasture is plentiful, i.e. South
America and New Zealand, or as a niche market alternative in the U.S. No differences in quality
grade were reported for cattle fed to equal ending live, but cattle fed pelletized alfalfa took 35
days longer to reach the target ending live weight and were tougher (less tender) in sensory panel
evaluations than concentrate-fed cattle (Oltjen et al., 1971). Steaks from cattle fed an alfalfa
silage based diet did not differ in WBSF when compared with steaks from cattle fed concentrate
based diet; however, forage-fed cattle had reduced ADG (-0.29 kg/d) and reduced HCW (-44.6
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kg), when compared with concentrate-fed cattle, slaughtered at an equal number of days on feed
(Mandell et al., 1998). Overall, forage-fed cattle were slower growing (reduced ADG) when
compared with concentrate-fed cattle; however tenderness results were inconsistent.
Time on Feed
The length of time cattle are fed an increased concentrate diet can alter palatability of the
meat from the animal, particularly tenderness. Warner-Bratzler shear force was reduced by 0.44
kg in yearling-fed cattle fed a concentrate diet for 139 days prior to slaughter compared with
yearling-fed cattle fed for 251 days (Epley et al., 1968). Others reported WBSF for steaks from
calf-fed Hereford cattle decreased by approximately 1.5 kg from 120 to 150 days on feed, but
remained unchanged until 240 days on feed at which WBSF increased by approximately 1.5 kg
(Zinn et al., 1970a, b). Short et al. (1999) reported that steaks from calf-fed Charolais crossbred
cattle were most tender after cattle were fed a concentrate diet for 270 days, however, yearling-
fed cattle reached the same level of tenderness after 90 days on a concentrate diet. Others
reported no differences in WBSF of steaks from yearling cattle fed for 100 to 160 days (Tatum et
al., 1980; Van Koevering et al., 1995). The optimum time on feed for cattle varies greatly
among studies due to starting live weight, genetic potential, or physiological age; however, the
aforementioned indicate that the optimum time on feed to achieve maximum tenderness in
yearling-fed British cattle breeds is within 90 to 160 days.
Average Daily Gain
Altering ADG can affect WBSF by changing the rate of protein turnover. Correlations
between ADG and 7 d WBSF have been reported (r = -0.17; Smith et al., 2007). Other studies
have reported stronger relationships between 7 d WBSF and ADG (r = -0.40; Shackelford et al.,
1994b). Both studies indicated as ADG increased WBSF decreased. However, most studies
have evaluated the relationship between ADG and tenderness were confounded with sources of
7
energy as discussed in the previous section; therefore, the results are not conclusive. Other
researchers reported cattle fed a high concentrate diet to achieve an ADG of 1.34 kg/d for 210
days produced steaks with WBSF values of 1.84 kg less than cattle fed a low concentrate diet
(ADG of 0.70 kg/d) for 230 days; however, marbling score was greater for cattle with an ADG
of 1.34 kg/d compared with ADG of 0.70 kg/d, small (4.99% extractable lipid) compared with
traces (2.48% extractable lipid), respectively (Savell et al., 1986; Aberle et al., 1981). Other
studies reported Friesian calves fed two dietary energy levels to attain growth rates of 0.77 and
1.20 kg/d until an ending live weight of 250 kg had a WBSF of 1.3 kg less for the faster growing
calves, and also reported a 0.70 correlation coefficient between ADG and sensory panel
tenderness (Therkildsen et al., 2002a, b). However, other studies reported cattle provided ad
libitum access to feed for 120 days with ADG of 0.34, 0.77, and 1.42 kg/d did not differ in
WBSF (Fishell et al., 1985). Another study fed cattle (beginning body weight 280 kg) 4
different dietary energy levels to achieve ADG of -0.21, 0.81, 1.18, and 1.35 kg/d for up to 175
days without finding any differences in WBSF (Burson et al., 1980).
Meat Characteristics
Muscle Proteins
Skeletal muscle contains 3 categories of proteins: sarcoplasmic, myofibrillar, and
stromal. Sarcoplasmic proteins include the glycolytic and proteolyic enzymes along with other
cytoplasmic proteins. Sarcoplasmic proteins comprise 30 to 35% of the protein in muscle tissue
(Goll et al., 2008). Myofibrillar proteins include both contractile and cytoskeletal proteins and
are responsible for the contractile properties of the myofibril. Myofibrillar proteins are the
largest class of proteins in skeletal muscle and comprise 55 to 60% of the protein in muscle
8
tissue (Goll et al., 2008). Stromal proteins are extracellular proteins, primarily collagen.
Stromal proteins comprise 10 to 15% of the protein in muscle tissue (Goll et al., 2008).
Sarcoplasmic proteins are located in the intracellular fluid and are responsible for
metabolic pathways in the cell (Lawrie and Ledward, 2006). Cathepsins, caspases, proteasomes,
and calpains are proteolyic enzymes found in the sarcoplasm of muscle cells (Lawrie and
Ledward, 2006). Cathepsins are lysosomal bound proteases that function at an optimal pH
between 3.5 and 6.5 (Goll et al., 2008). Cathepsins are inhibited by cystatin (Koohmaraie et al.,
1991b). Cystatin is a sarcoplasmic protein in the intracellular fluid; therefore, cathepsin activity
outside of a lysosome is unlikely due to the presence of cystatin (Koohmaraie et al., 1991b).
Caspases are proteases involved in apoptosis of cells. Caspases require signaling from either
extrinsic or intrinsic pathways to become active. Proteasomes are protein complexes that
function to degrade damaged or excess proteins in a cell. Proteasomes are responsible for
targeted protein degradation and require proteins to be labeled with ubiquitin prior to
degradation. Calpains are calcium dependent proteases. Different calpain isoforms require
different concentrations of calcium to become active. Calpain 1 (µ-calpain) requires a calcium
concentration of 50 to 100 µM to become active; whereas, calpain 2 (m-calpain) requires a
calcium concentration of 1000 to 2000 µM to become active (Du and McCormick, 2009).
Calpain activity is mainly regulated by calcium concentration and calpastatin, which is an
inhibitor of calpains.
Myofibrillar proteins comprise the majority of protein in muscle tissue and are
responsible for the contractile function of the muscle cell (Taylor et al., 1995). The location of
the myofibrillar proteins is presented in Figure 1. Myosin (thick filament) is the most abundant
protein in skeletal muscle and is responsible for converting chemical energy to mechanical
9
energy during muscle contraction. Actin (thin filament) is a globular protein used along with
myosin during muscle contraction. Tropomyosin is wrapped around the actin filament covering
the myosin binding site during the resting phase (Arakawa et al., 1970). The movement of
tropomyosin from the actin binding site is controlled by troponin proteins (troponin I, troponin T,
and troponin C) located on the tropomyosin filament. Alpha-actinin is a cytoskeletal protein
responsible for binding actin filaments to the Z-disk. Nebulin runs the length of the actin
filament and maybe responsible for regulating the length of the actin filament. Titin, one of the
largest proteins in a muscle cell, spans from the Z-disk to the M-line of a sarcomere and
regulates the resting length of a sarcomere. Costameres are structural components of skeletal
muscle that bind the Z-disk to the sarcolemma of muscle cells. Costameres are comprised of
desmin, vinculin, ankyrin, dystrophin, γ-actin, β-spectrin, and talin (Taylor et al., 1995). Desmin
also serves to maintain structural integrity by binding adjacent Z-disk within a muscle cell
together (Taylor et al., 1995).
Sarcomere Length
A sarcomere is considered the basic unit of muscle contraction and is defined as the
distance between adjacent Z-lines (Lawrie and Ledward, 2006). A sarcomere is comprised
mainly of myosin filaments (thick) and actin filaments (thin). Myosin filaments are contractile
proteins and are approximately 1.5 µm in length and make up part of the A-band along with actin
filaments. The A-band is the area in which actin and myosin filaments interact during
contraction. In a relaxed sarcomere, the overlap of actin and myosin filaments is reduced
allowing for the H-band to increase in size. The H-band is comprised of myosin and spans the
center of the sarcomere or M-line. The I-band is located on each end of a sarcomere and actually
crosses adjacent Z-lines and is comprised of actin filaments. The I-band contains the structural
attachment of actin filaments to the Z-line at each end of the sarcomere. Actin filaments are
10
approximately 1.0 µm in length. The length of the actin and myosin filaments along with the
structural proteins of the Z-line contribute to 3.6 µm of a fully stretched sarcomere and to 1.6 µm
of a fully contracted sarcomere. However, studies have reported sarcomere lengths of 1.44 µm
indicating that the myosin filament has ruptured through the Z-line (Grayson and Lawrence,
2013). Sarcomere lengths in the longissimus muscle range from 1.53 to 2.08 µm (Smulders et
al., 1990). Postmortem ATP depletion in muscle causes the formation of permanent bonds
between actin and myosin in the sarcomere; this is termed rigor mortis (Lawrie and Ledward,
2006). Once the rigid actomyosin bonds are formed, the lengths of the sarcomere can no longer
change. Sarcomere length depends on the position of the muscle within a carcass at the onset of
rigor mortis; therefore, muscle in a state of compression will have shorter sarcomeres than
muscles in a state of tension.
The effects of sarcomere length on shear force have been examined in studies comparing
Achilles carcass suspension (carcass is suspended by the Achilles tendon, conventional method
in North America) to Tenderstretch carcass suspension (carcass is suspended by the obturator
foramen). Achilles suspension resulted in shorter sarcomere lengths in the longissimus dorsi as
compared with Tenderstretch suspension, 1.80 µm and 2.31 µm respectively, causing the longer
sarcomeres to have more desirable tenderness scores and reduced Volodkevich Bite
Tenderometer values by 18% at 2 d and 14% at 7 d postmortem (Joseph and Connolly, 1977).
No measurements of proteolysis were performed in the previous study (Joseph and Connolly,
1977); therefore the decrease in magnitude of the difference between the suspension methods at
increased aging time could be caused by proteolysis. Another method to increase sarcomere
length in the longissimus muscle is the Tendercut method in which the vertebral column is cut
leaving the weight of the fore-quarter suspended by the longissimus muscle. Beaty et al. (1999)
11
reported a sarcomere length increase of 0.18 µm in the longissimus thoracis and reduced WBSF
by 1.85 kg when using the Tendercut method. Others reported that by adding a device to
manipulate the hanging position of the fore shank of the carcass prior to the onset of rigor mortis,
sarcomere length increased by 0.78 µm and reduced 7 d WBSF by 0.69 kg in the serratus
ventralis (Grayson and Lawrence, 2013). Shackelford et al. (1994b) reported a -0.18 correlation
coefficient between WBSF at 7 d postmortem and sarcomere length, reporting as sarcomere
length increased, WBSF was reduced. In summary, changes in sarcomere length impact meat
tenderness; however, sarcomere length can only account 30% of the variability in sensory panel
tenderness evaluations (Smulders et al., 1990).
Protein Turnover
Postnatal muscle growth occurs mainly by an increase in cell size (hypertrophy). As a
result, the relationship of protein synthesis to protein degradation is important to the rate of
postnatal muscle hypertrophy. Hypertrophy occurs when protein synthesis is greater than protein
degradation. Researchers have proposed myofibrillar proteins are degraded causing
myofilaments to be released from the sarcomere (Goll et al., 2008). In this method, calpains
cleave titin, nebulin, desmin and filiman from the Z-disk causing the Z-disk to no longer be
tethered to the sarcolemma (Goll et al., 2003). Calpains facilitate the release of the thin filament
by degrading tropomyosin and troponin (Goll et al., 1992). Calpains cleave the proteins
associated with the M-line causing the release of the thick filament (Goll et al., 2008). Calpains
do not degrade proteins to amino acids (AA); however, the release of the proteins from the
myofibril allows proteasomes to degrade the proteins to AA (Goll et al., 2008). Therefore,
protein turnover may be regulated by calpastatin activity or expression. Beta-adrenergic agonists
decrease protein degradation in most meat producing species (Koohmaraie et al., 2002).
Administration of beta agonists can cause an increase in calpastatin activity and gene expression
12
(Parr et al., 1992). Decreased protein degradation involved in protein turnover in the animal
during hypertrophy negatively impacts postmortem protein degradation and results in a less
tender product (Koohmaraie et al., 1991a).
Protein Degradation
The degradation of cytoskeletal proteins (titin, nebulin, filamin, vinculin, desmin, and
dystrophin) by proteases is considered to be the main reason meat becomes more tender during
postmortem aging. Three proteolysis systems have been studied, cathepsins, proteasomes, and
calpains. However, none of these proteolysis systems alone can explain all of the postmortem
changes in meat tenderness.
Calpains are calcium-dependent proteases involved in postmortem tenderization.
Calpains do not degrade actin and myosin; however calpains are responsible for Z-line
degradation (Taylor et al., 1995). The calpain system uses different isoforms of calcium-
dependent cysteine proteases to degrade cytoskeletal proteins (titin, nebulin, desmin, and
filamin) in muscle (Rowe et al., 2004). Many different isoforms of calpains have been
discovered over the past few decades. Calpains 1, 2, and 10 tend to cause postmortem
tenderization in meat by degrading nebulin and desmin (Goll et al., 2003), with calpain 10 being
the most correlated with increased tenderization (Ilian et al., 2004). As muscle is converted to
meat, changes in the microenvironment cause an increase in oxidation (Rowe et al., 2004).
Oxidation can affect the activity of µ- and m-calpain (1- and 2-calpain) by oxidizing a cysteine
in the active site (Guttmann et al., 1997). Calpains require the cysteine in the active site to be
reduced for activation. The release of Ca++ ions from the sarcoplasmic reticulum causes
calcium-activated sarcoplasmic factors to become active postmortem, i.e. calpains and
calpastatin. Calcium is released from the sarcoplasmic reticulum as postmortem carcass
13
temperature decreases. Therefore, the concentration of sarcoplasmic free calcium in beef muscle
changes from 16 µM at 40 min postmortem to 210 µM by 4 days postmortem (Ji and Takahashi,
2006). Calpain 1 has maximum activity at a calcium concentrations between 50 and 100 µM;
therefore, calcium concentrations postmortemly allow for maximum activity caplain 1. Calpain
2 has maximum activity at calcium concentrations between 1000 and 2000 µM; therefore,
calpain 2 may not be active postmortem (Du and McCormick, 2009). Calpains do not continue
to function for extended periods of time, since calpains will autolysis (Dransfield, 1992; Du and
McCormick, 2009). Calpastatin inhibits calpains from binding to structural proteins in
myofibrils (Mellgren et al., 1989).
Shackelford et al. (1994a) reported a genetic correlation of rg = 0.50 and phenotypic
correlation of rp = 0.27 between calpastatin activity and 7 d WBSF. O'Connor et al. (1997)
reported calpastatin activity had greater phenotypic correlations with WBSF at less than 7 days
postmortem (r ≥ 0.40); whereas, correlations at 14 d and 21 d were r = 0.27 and r = 0.24,
respectively. Other studies reported a similar correlation between calpastatin activity and 7 d
WBSF; however, the correlation for WBSF at 14 d postmortem was greater (r = 0.44; Riley et
al., 2003). Another study reported a correlation of r = 0.15 between 14 d WBSF and calpastatin
activity (Wulf et al., 1996). The mean calpastatin activity reported by Shackelford et al. (1994a)
was 2.8 units/gram; whereas, all other studies discussed (O’Connor et al., 1997; Wulf et al.,
1996; Riley et al., 2003) had calpastatin activities greater than 3.26 units/gram. Therefore as
time postmortem increases, the correlation between calpastatin activity and WBSF decreases.
Proteolysis of the structural proteins causes a weakening of the myofibril; thereby,
increasing tenderness (Laville et al., 2009). Different species undergo postmortem tenderization
at different rates with pork becoming tender at a faster rate than lamb, and beef becoming tender
14
at a slower rate than either pork or lamb (Koohmaraie et al., 1991b). Shackelford et al. (1994a)
reported a genetic correlation of -0.52 between ADG and calpastatin activity; whereas, the
phenotypic correlation was -0.15. Another study reported genetic correlation lower than
previously stated (rg = -0.18) along with the phenotypic correlation (rp = 0.03; Smith et al.,
2007). The previously stated correlations support the hypothesis that faster growing cattle
should be more tender, because increased calpastatin activity negatively impacts protein
degradation and tenderness.
Carcass parameters have a role in postmortem tenderization by either affecting pH
decline or enzymatic activity. Calpastatin is better able to inhibit calpains at a greater pH, but as
pH declines and Ca++ ion concentration in the sarcoplasm increases postmortem, the ability of
calpastatin to inhibit calpains is reduced (Purchas et al., 1999). In a study comparing the effects
of electrical stimulation in Bos indicus and Bos Taurus cattle, a faster pH decline along with a
faster chill rate caused calpains to quickly overcome the inhibitory effects of calpastatin, but a
decreased temperature decline and decreased pH levels caused calpains to prematurely degrade
(Hwang and Thompson, 2001). Crouse et al. (1991) reported chill rate was related to both
adjusted fat thickness and loin muscle (LM) area with correlation coefficients of 0.49 and 0.54,
respectively. Carcasses having a greater fat thickness will have a reduced chill rate due to the
insulative properties of fat; whereas, heavier carcasses will also chill at a slower rate than smaller
carcasses due to the amount of heat energy in the carcass. The myofibril fragmentation index
can serve as a method to measure proteolysis, but may not be as accurate in carcasses that have
undergone electrical stimulation (a process in which carcasses are electrically stimulated prior to
the onset of rigor mortis to speed the onset of rigor mortis and increase the rate of pH decline),
since stimulation may rupture myofibrils.
15
Cathepsins are stored in lysosomes in the sarcoplasmic reticulum, but require postmortem
disruption of lysosomes in order to be released with disruption occurring more readily at greater
temperatures (Dutson, 1983). A review of cathepsins reported no impact on postmortem
proteolysis at a normal ultimate pH of approximately 5.4; however, cathepsins are active at pH
levels between 4.0 and 6.0 but activity increases as muscle conditions become more acidic
(Dutson, 1983; Hopkins and Thompson, 2002).
Proteasomes such as multicatalytic protease complex can degrade myofibrillar proteins
(nebulin and troponin T; Lamare et al., 2002). Proteasomes have significant activity across
normal pH range of postmortem muscle and remain active for approximately 7 days postmortem
(Lamare et al., 2002). However, the size of the multiple catalytic core of the proteasome
complex prevents the degradation of intact myofibrils (Lee et al., 2010). Therefore, proteasomes
may be involved in further degradation after the myofibrils have been disrupted by other
proteases.
Rate of pH Decline
Anaerobic glycolysis uses glycogen to produce ATP and lactic acid causing a decline in
muscle pH (Lawrie and Ledward, 2006). The ultimate pH (UPH) can cause denaturation, a
change in quaternary or tertiary structure, of muscle protein and change the rate or degree for
proteolysis (Laville et al., 2009). Changes in UPH may affect the solubility or activity of
proteolyic enzymes. Purchas et al. (1999) found that an UPH of approximately 5.9 resulted in
greater shear force values relative to UPH of 6.8 and 5.6, independent of aging time. Wulf et al.
(2002) reported beef with a pH of 6.00 had a 1.75 kg greater WBSF as compared with beef with
a pH of 5.53. Another study reported a low correlation (r = 0.14, P < 0.01) between 7 d WBSF
and 48 hrs postmortem pH (Shackelford et al., 1994a). Myofibrillar fragmentation decreased at a
16
pH of 5.9 as compared with pH between 6.4 to 7.0 and 5.4 to 5.6 (Purchas et al., 1999). Calpains
have an optimal pH range between 7.2 and 7.8 with activity decreasing as pH decreases;
however, at pH below 5.7 calpastatin activity is decreased (Kendall et al., 1993). The increased
tenderness at pH levels greater than 6.4 could be associated with increased calpain activity since
the pH is closer to the optimum pH range. Whereas, the increase in tenderness at pH levels less
than 5.7 could be attributed to decreased calpastatin activity. Therefore, UPH may have a role in
rate or extent of postmortem tenderization.
Muscle Fiber Type
Muscle fibers are classified by two methods, either identifying the myosin heavy chain
present in the myofibril or staining to measure myosin ATPase activity and metabolic activity.
When classified by myosin heavy chain, four different myosin heavy chains (MyHC-2a, -2x, -2b,
and -1) are used to differentiate muscle fibers: Type I (MyHC-1) slow red oxidative fibers, Type
IIa (MyHC-2a) fast white glycolytic fibers with intermediate oxidative capability, Type IIx
(MyHC-2x) are similar to Type IIa but have a faster contraction speed, and Type IIb (MyHC-2b)
are faster than Types IIa or IIx but have limited oxidative metabolism (Du and McCormick,
2009; Lawrie and Ledward, 2006). When classified using staining methods, myosin ATPase
activity staining is used to determine rate of contraction either slow β fibers or fast α fibers;
whereas, metabolic staining with NADH dehydrogenase or succinate dehydrogenase is used to
determine oxidative capacity, either R for strong oxidative capability or W for weak oxidative
capability (Du and McCormick, 2009). Different muscles have different ratios of glycolytic to
oxidative muscle fibers; therefore, the ratio of these fiber types could also explain the differences
in postmortem tenderization. Ouali (1990) reported in a review, glycolytic fibers had a faster
rate of postmortem tenderization than oxidative fibers. However, Ouali (1990) reported calpain
concentration was greater in oxidative fibers than in glycolytic fibers, but the calpain activity in
17
oxidative fibers was reduced or inhibited. In agreement, Whipple et al. (1990) reported a 0.26
correlation coefficient between WBSF and percentage αW fibers and a correlation coefficient of
-0.27 between WBSF and percentage αR fibers. Calkins et al. (1981) reported a 28.85
coefficient of determination (R2) for shear force of cattle with overall maturity score of A when
using percentage of αW fiber, mean αW fiber area, and total αW fiber area; whereas, in all cattle
maturities examined a coefficient of determination of 18.71 was reported when using mean βR
fiber diameter, total αR fiber area, percentage αR fiber area, and percentage α-white fiber area.
Muscle Fiber Diameter
Muscle fiber diameter may play an important role in overall meat tenderness. Each
muscle consists of different fiber diameters based on function of the muscle and fiber types
present. Different fiber types greatly impact muscle fiber diameter with βR being approximately
half the diameter of αW in the longissimus lumborum of cattle (Du and McCormick, 2009).
Muscles that are used for more controlled movements have smaller fiber diameters; whereas,
muscles that are used for locomotion have larger fiber diameters (Lawrie and Ledward, 2006).
An early study by Hiner et al. (1953) reported as cattle increase in age, the diameter of the
muscle fibers increase. Additionally, Tuma et al. (1962) reported as cattle increase in age, the
diameter of muscle fibers increase; however, the standard deviation for muscle fiber diameter
increases with age. The increase in diameter with an increase in age is expected because muscles
increase in size via muscle cell hypertrophy. Many studies have reported correlation coefficients
greater than 0.51 between longissimus shear force and muscle fiber diameter (Hiner et al., 1953;
Tuma et al., 1962), indicating an increase in fiber diameter increases shear force. Crouse et al.
(1991) reported muscle fiber size is important in tenderness of meat at 1 and 3 days postmortem
and becomes less important at 6 and 14 days postmortem. The effects of muscle fiber diameter
agree with the findings concerning muscle fiber type.
18
Connective Tissue
Collagen is an important means of support and is involved in the transfer of contractile
force in muscles. Twenty-nine different forms of collagen have been identified, with each type
having a different primary structure (Du and McCormick, 2009). Bailey and Light (1989)
reported that four types of collagen, distinguished by alpha chains, Type I ([𝛼1(𝐼)]2𝛼2), Type II
([𝛼1(𝐼𝐼)]3), Type III ([𝛼1(𝐼𝐼𝐼)]3), and Type IV (𝛼1(𝐼𝑉)[𝛼2(𝐼𝑉)]2), are important in meat
tenderness. Collagen in muscle tissue can be classified into four layers: epimysium, perimysium,
endomysium, and basement membrane. The epimysium is the outer most layer of connective
tissue surrounding an individual muscle; whereas, the perimysium surrounds individual groups
of muscle fibers and contains larger blood vessels and nerves (Lawrie and Ledward, 2006). The
endomysium surrounds each muscle fiber and is a thin non-fibrous sheath (Lawrie and Ledward,
2006). The basement membrane surrounds the sarcolemma complex of a myofibril and is
composed of less than half collagen (Lawrie and Ledward, 2006). Each layer is formed by
different types and amounts of collagen (Bailey et al., 1979; Rowe, 1981; Light and Champion,
1984; Light et al., 1985; Lawrie and Ledward, 2006). The epimysium comprises 1.2% of the dry
muscle weight, and is comprised of 22.2% collagen (Light and Champion, 1984). Type III
collagen is 16.4% of the total amount of Type I and III collagen in the epimysium (Light and
Champion, 1984). The perimysium comprises 4.7% of the dry muscle weight, and is comprised
of 95.3% collagen (Light and Champion, 1984). Type III collagen is 28.0% of the total amount
of Type I and III collagen in the perimysium (Light and Champion, 1984). The endomysium
comprises 0.3% of the dry muscle weight, and is comprised of 42.4% collagen (Light and
Champion, 1984). Type III collagen is 62.3% of the total amount of Type I and III collagen in
the endomysium (Light and Champion, 1984). The connections between each layer of connective
tissue allow for the transfer of force required for movement.
19
Tropocollagen is formed by individual chains of collagen forming a left-handed helix and
then three left-handed helical chains intertwining to form a larger right-handed helix (Lawrie and
Ledward, 2006). Tropocollagen molecules aggrade together to form a fibril, and with fibrils
aggrade together to form fibers (Lawrie and Ledward, 2006). However, with all the different
forms and types of collagen, the proportion in muscle is only about two percent (Bailey, 1985;
Light et al., 1985). Of the two percent collagenous tissue in muscle, elastin comprises
approximately two to three percent of the total amount (Du and McCormick, 2009). Studies
have reported a lack of relationship between total collagen and tenderness (Cross et al., 1973;
Bailey, 1985); however, research has been conducted to examine the effect of changes in the
ratio of Type III to Type I collagen in muscle tissue on WBSF,
Light et al. (1985) reported no effect of Type III collagen fiber content on the texture of
six bovine muscles. Burson et al. (1986) reported the percentage of Type III intramuscular
collagen in longissimus muscle does not affect WBSF, but as the percentage of Type III
intramuscular collagen increased collagen solubility decreased (r = -0.49). Meat tenderness may
be more related to collagen crosslinking than amount of collagen present.
As an animal ages, the number of collagen cross links increases; however, not all
collagen cross links impact tenderness equally. Lawrie and Ledward (2006) reported the
formation of non-reducible links among three or more chains causing the formation of a three-
dimensional structure greatly increased tensile strength. The formation of these three-
dimensional structures is considered the maturation of collagen. Thus, as the number of mature
bonds in collagen increases, the amount of thermal shrinkage increases when meat is cooked
(Lawrie and Ledward, 2006; Lepetit, 2007). As the number of mature collagen cross links
increases, meat becomes tougher. As meat is cooked, some immature collagen becomes soluble.
20
Purslow (2005) reported as cooking temperature increased from 20 to 50 oC, the perimysium
strength increased, but decreased at temperatures greater than 50 oC. Lepetit (2007) reported a
0.83 correlation coefficient between the sum of heat stable collagen cross links (Pyridinoline,
Ehrlich Chromogen, and Dihydroxylysinorleucine; µmole per gram of wet muscle) and WBSF.
Also, the rate of collagen turnover has a role in tenderness. As animals age, collagen
must be remodeled to allow for the increase in size of the muscle fibers (Purslow, 2005). Miller
et al. (1987) reported a 1.0 kg decrease in shear force and an increase in soluble collagen in
mature cows fed a high energy diet for 84 days to achieve an ADG of 1.0 kg/d as compared to
mature cows fed a low energy diet to achieve an ADG of 0.1 kg/d. Duckett et al. (2007) reported
an increase in total collagen between yearling cattle finished at two different growth rates;
however, no differences in soluble collagen or 14 d WBSF were reported. These finding indicate
that soluble collagen percentage impacts WBSF; however, yearling cattle do not differ in soluble
collagen percentage.
Immunoblotting
Protein Extraction and Solubilization
Protein identification requires proteins to be extracted from muscle tissue and solubilized
to prevent aggregation. Proteins are extracted by mechanically lysing the cells in a buffer
solution containing a detergent. The buffer solution increases the solubility of proteins by
optimizing the pH or ionic strength of the solution. Detergents disrupt hydrophobic interactions
between proteins to increase the solubility of the protein in aqueous extraction solutions.
Sodium dodecyl sulfate (SDS) is a detergent used to disrupt non-covalent tertiary protein bonds;
therefore, SDS causes disruption of the native confirmation of the protein. The disruption of the
native confirmation causes enzymes in the samples to denature and become inactive. The
21
denaturing of enzymes prevents further proteolysis from occurring. After extraction, the tertiary
and secondary structures are denatured by heating the samples in the presence of SDS and beta-
mercaptoethanol. Beta-mercaptoethanol aids in breaking of the disulfide bonds. Sodium dodecyl
sulfate causes the proteins to have a negative net charge. The SDS creates a charge to mass ratio
that is approximately the same for all proteins or polypeptide chains in the sample (Switzer and
Garrity, 1999).
Protein Separation
Sodium dodecyl sulfate – polyacrylamide gel electrophoresis (SDS-PAGE) is used to
separate proteins based on molecular weight. Anionic proteins are separated using an electric
current flowing through a polyacrylamide gel. Protein samples along with a molecular weight
standard are pipetted into the well of a polyacrylamide gel. Molecular weight standard is a
mixture of purified proteins with known molecular weights. Comparison of the molecular
weight standards to the samples protein allows for the determination of relative protein weight.
Proteins will group together based on molecular weight and move through the polyacrylamide
gel toward the positively charged electrode. The mobility of the proteins is regulated by the
friction between the protein and the acrylamide in the gel. Therefore, polyacrylamide gels are
formulated to separate proteins in a specific weight range. Large proteins (45 to 200 kDa) are
separated using polyacrylamide gels containing 7.5% acrylamide; whereas, small proteins (5 to
45 kDa) are separated using gels containing 20% acrylamide (Switzer and Garrity, 1999). The
voltage applied to the gel controls the speed of the interactions between the proteins and the
acrylamide. As voltage increases the overall rate of proteins migrating through the gel will
increase. At high voltages, protein resolution will decrease because of increased temperature of
the gel. Temperature of the gel should not exceed 35 to 40 ºC (Schagger, 2006). Also, high
voltages can cause gels to melt. However, low voltages cause protein bands to broaden and could
22
result in samples overlapping. After proteins are separated in SDS-PAGE gels, proteins are
electrophoretically transferred to either polyvinylidene fluoride (PVDF) or nitrocellulose
membranes for protein identification.
Western Blotting
Western blotting uses antibodies to identify specific proteins. The membranes are
incubated in a blocking solution to saturate all of the membrane not already binding sample
proteins. Blocking solutions must not contain any proteins of interest or protein that can bind
with the antibodies used to identify the proteins. Non-fat dry milk is commonly used in a
blocking solution when researchers are working with muscle proteins. The proteins in the non-
fat dry milk bind to the remaining areas of the membrane without transferred muscle proteins.
After blocking, membranes are incubated in a solution containing a monoclonal antibody
(primary antibody) of the protein of interest. Monoclonal antibodies are produced from identical
B-lymphocytes and have the same binding affinity for the same epitope on a protein. Therefore,
monoclonal antibodies are able to bind very specifically to an epitope on a protein. Membranes
are washed to remove any unbound or non-specifically bound primary antibody. A solution
containing a polyclonal antibody (secondary antibody) specific to epitopes on the primary
antibody is applied to the membrane. The polyclonal antibodies are covalently conjugated to an
enzyme, such as horseradish peroxidase (Switzer and Garrity, 1999). Membranes are then
washed to remove any unbound or non-specifically bound secondary antibody. The substrate for
the enzyme on the secondary antibody is applied to the membrane causing the enzyme to emit a
luminescent signal. The luminescent signal is visualized using a gel imaging system. Images of
the gel are subjected to densitometry analysis to quantify the strength of the signal. The strength
of the signal of the sample is compared to the signal strength of a standard on the same gel. The
ratio of the signal strengths is reported as the ratio to reference of the sample.
23
Immunoblotting has proven to be a valid method used for identification of proteins or
polypeptide fragments from proteins. Immunoblotting allows researchers to measure the percent
change in proportion of an intact protein or polypeptide fragments from a degraded protein to a
reference sample. Therefore, immunoblotting can be used to determine changes in a protein at
specific time points during postmortem aging. One limitation to immunoblotting is direct
comparisons of the values from immunoblotting across studies can be difficult. Reference
standard used to determine the proportion are not standardized among studies.
Statistical Analysis
Statistical Power Analysis
Statistical power is the ability to reject a hypothesis that states 2 treatments are the same
and conclude the treatments are different. Statistical power calculations balance the probability
of a Type I error (alpha, 1 – confidence level, concluding two treatments different when the
treatments are the same) and the probability of a Type II error (beta, concluding 2 treatments are
the same when the treatments are different). Statistical power allows for the calculation of
sample size needed to detect a difference between treatments based on a population’s standard
deviation of the term to be analyzed. Consumers are able to discern a 0.4 kg difference in WBSF
of longissimus steaks (Igo et al., 2011). Meyer et al. (2005) reported a standard deviation of 0.74
kg in WBSF at 21 d postmortem (n=6). Power of the test is calculated using the equation,
𝑃𝑜𝑤𝑒𝑟 = 1 − 𝛽
𝛽 ≈ 𝑃
[
𝑍 <
[
𝑑
√2𝜎2
𝑁/2⁄]
− 𝑍𝑎2
]
24
where P is the probability of the Z value, N is the number of animals, σ is the standard deviation,
and d is the mean difference of interest (Ott and Longnecker, 2001). Therefore, a sample size of
32 animals would have a power to detect a difference of 0.32 kg of WBSF. If the mean
difference is increased to 0.75 kg, power would increase to 0.79. Sample size at a desired power
can be calculated using the equation,
𝑁 = 2 ∗2𝜎2 ∗ (𝑍𝛽 + 𝑍𝛼
2)2
𝑑2
Therefore, a calculated sample size of 110 animals would be needed to detect a mean difference
of 0.4 kg, with standard deviation of 0.74 kg at an alpha of 0.05 and beta of 0.80. As
demonstrated, power is a relationship between the expected mean difference, number of
observations used, and standard deviation of the population.
Regression Analysis
Regression is a statistical analysis that is used in determining the relationship between
variables to predict an outcome (Kutner et al., 2005). Linear regression coefficients are
determined by the ordinary least squares (OLS) method. Ordinary least squares requires the data
to be unbiased and x variables uncorrelated (r = 0). Ordinary least squares method uses sum of
the squared deviations between the observed values from the expected values (Q). The least
squares criterion Q is calculated as follows,
𝑄 = ∑(𝑌𝑖 − 𝛽0 − 𝛽1𝑋𝑖)2
𝑛
𝑖=1
where, β0 is the coefficient of the intercept, β1 is the coefficient of the slope, Yi is the value of the
ith observation of the dependent variable, and Xi is the value of the ith observation of the
25
independent variable (Kutner et al., 2005). Therefore, the best estimates of β0 and β1 are
obtained when Q is minimized. The efficiency of regression functions is evaluated based on the
coefficient of determination (R2), the amount of variation in the data accounted for in the fitted
function. The coefficient of determination is
𝑅2 =𝑆𝑆𝑅
𝑆𝑆𝑇𝑂
where,
𝑆𝑆𝑅 = ∑(�̂�𝑖 − �̅�)2
𝑖
and
𝑆𝑆𝑇𝑂 = ∑(𝑦𝑖 − �̅�)2
𝑖
Therefore, the coefficient of determination increases as the sum of squares regression (SSR)
increases in relation to the sums of squares total (SSTO). Multiple linear regression evaluates
the effect of multiple predictor variables on the response variable. For multiple regression, the
model is
𝑌𝑖 = 𝛽0 + 𝛽1𝑋𝑖1 + ⋯+ 𝛽𝑝−1𝑋𝑖,𝑝−1 + 𝜀𝑖
where, p is the number of predictor variables in the model (Kutner et al., 2005). The coefficient
of determination is not an accurate means to determine the efficiency of a multiple linear
regression model. The coefficient of determination will increase as predictor variables are added
to the model even if the added predictor variable does not account for variation in the model.
Therefore, an adjusted coefficient of determination is used to account for the increased
26
complexity of the model when additional predictor variables are added. The adjusted coefficient
of determination is
𝐴𝑑𝑗. 𝑅2 = 1 − (1 − 𝑅2)𝑛 − 1
𝑛 − 𝑝 − 1
Adjusted coefficient of determination penalizes for the addition of predictor variables to the
model that do not account for variation. However, the coefficient of determination can be
influenced by data points unequally.
Observations made at extremes can have increased influence (leverage) on the fitted
function since no other points are able to mitigate the observations effect. Statistical tests,
DFBETA and DFFITS, are used to measure influence on the fitted function. The DFBETA
analysis measures the leverage of the X(i) observation on the regression function coefficients
when the X(i) observation is removed. An observation is considered to have significant leverage
on the regression function coefficient if the absolute DFBETA value is greater than 1 in small to
medium data sets (less than 30 observations) or greater than 2/√𝑛 for large data sets (greater
than 30 observations), where n is the number of experimental units (Kutner et al., 2005;
Cousineau and Chartier, 2010). The DFFIT analysis measures the difference between the fit
value Yi and the predicted value Yi(i) when ith case is omitted (Kutner el al., 2005). An
observation is considered to be influential if the absolute DFFIT value is greater than 1 for small
to medium data sets (less than 30 observations) or greater than 2/√(𝑝
𝑛) for large data sets (more
than 30 observations), where p is the number of parameters in the regression model and n is the
number of experimental units in the data set (Kutner el al., 2005; Cousineau and Chartier, 2010).
27
Multicollinearity
In multiple linear regression, correlation between predictor variables, multicollinearity,
can affect the regression coefficients and the fit of the function. Multicollinearity causes the
standard errors to be inflated; therefore, the parameters estimates (b) are not stable. Variance
inflation factors (VIF) are used to measure how variance of the regression coefficients are
inflated by linear relations in predicator variables. When VIF values are greater than 10,
regression coefficients may not be significant due to the inflation of the estimated standard
deviations. Remedial measures to correct for multicollinearity include removing predictor
variables from the model or using ridge regression (Kidwell and Lynn Harrington, 1982).
Removal of one or more predictor variables can reduce multicollinearity; however, removal of
predictor variables has limitations. Dropped predictor variables do not add information to the
model and can affect the estimates of the regression coefficients (Kutner et al., 2005). Ridge
regression uses a modified OLS method to determine the estimates of the coefficients (Kidwell
and Lynn Harrington, 1982). The modified OLS introduces a constant bias (c) to minimize Q.
The value for c is determined using ridge trace and VIF. Ridge trace simultaneously plots
estimated ridge standardized regression coefficients for different values of c (Kutner et al.,
2005). As c increases, the ridge estimated standardized regression coefficients will stabilize and
the VIF values for the predictor variables will decrease. The smallest value for c is chosen based
on when the regression coefficients become stable and the VIF values are small. Therefore, the
estimates are bias estimators of the regression coefficients. Ridge regression limits ordinary
inference procedures and causes the distributional properties to be unknown (Kutner et al.,
2005). Therefore, caution must be taken when considering predictor variables for a regression
function.
28
Model Selection
For every set of predictor variables, 2p-1 different models can be made (Kutner et al.,
2005). Therefore, the evaluation of each combination of predictor variables is critical in
determining the best model to predict the dependent variable. Many different techniques have
been developed to select predictor variables for regression models. Models can be selected
based on the adjusted coefficient of determination. The adjusted coefficient of determination for
all models is determined and the model with the greatest adjust coefficient of determination is
chosen as the best model. Mallow’s Cp is used in model selection to minimize the number of
predictor variables in the final model. Mallow’s Cp compares the predictive ability of a model
containing a subset of the predictor variables to the predictive ability of the full model, model
containing all predictor variables. Mallow Cp is
𝐶𝑝 =𝑆𝑆𝐸𝑝
𝑀𝑆𝐸𝑓𝑢𝑙𝑙− (𝑛 − 2𝑝)
where, SSEp is the sums of squares error for the subset model and MSEfull is the mean square
error of the full model (Kutner et al., 2005). Mallow Cp values are compared to the number of
predictor variables in the model. Therefore, the model with Cp ≤ p is the best model for the
prediction. Three automatic methods of model selection, forward selection, backward
elimination, and forward stepwise regression, have been developed. Automatic selection
methods are used in data sets containing 30 or more predictor variables (Kutner et al., 2005).
Forward stepwise fits a linear regression model for each of the predictor variables. The model
with the greatest t* statistic, test if the slope is different from zero 𝑡𝑘∗ =
𝑏𝑘
𝑠{𝑏𝑘}, is selected as the
first predictor variable added to the model (Kutner et al., 2005). A linear regression model is
then refitted using the first predictor variable and each of the remaining predictor variables. The
29
selection process continues until none of the remaining predictor variables are less than the
predetermined alpha value. Backward elimination begins with a model containing all predictor
variables and evaluates each variable based on the significance. In each step, the least
significant, greatest P-value, predictor variable is dropped from the model. The model is then
refitted and the process continues until all variables in the model are significant at a
predetermined alpha level. Forward stepwise regression is a method that combines both forward
selection and backwards elimination. Forward stepwise regression develops regression models
by adding and deleting predictor variables one at a time based on a preset alpha level. Forward
stepwise regression continues to evaluate predictor variables until none of the remaining
predictor variables are significant at the preset alpha level and all the terms in the model are
significant at the preset alpha level. A major weakness to stepwise automatic selection methods
is that only one model is developed; therefore, other methods, Mallow’s Cp, should be used to
evaluate the model produced by the stepwise selection.
Conclusions
In general, finishing cattle on grain based diets, or aging beef for a specific number of
days cannot fully guarantee tenderness. Therefore, with the emergence of the USDA Guaranteed
Tender Beef program, research focusing on the relation of growth rate and carcass parameters
and their effect on tenderness is important. Furthermore, understanding how growth rate is
related to postmortem proteolysis may allow for more accurate predictions of beef tenderness to
be achieved.
30
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Figure 1. A diagram of the structure and protein location of a muscle cell. One sarcomere is
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38
Chapter 2
Characterizing the effect of average daily gain and carcass
parameters on shear force in finishing steers
Abstract The objectives of this study were to characterize the effects of rate of gain on WBSF in
finishing steers, characterize the effects of rate of gain on postmortem proteolysis, and to develop
a prediction equation using measured carcass traits to identify carcasses with a greater
probability of meeting consumer expectations for tenderness. Steers (N = 36, initial BW = 271 ±
24 kg) were stratified by weight into 6 pens of 6 steers (2 pens per treatment). Steers were limit
fed an common basal diet to target 3 rates of gain (1.0, 1.5, and 1.85 kg/d). Steers were weighed
every 14 d week throughout the study period to adjust feed amount, using the NRC equation for
NEg, based on pen mean BW. At study d 151, the 2 heaviest steers from each pen were
slaughtered. The middle two steer were slaughtered on d 161 with the remaining steers
slaughtered on d 167. Warner-Bratzler shear force was determined at 6 aging points (1, 7, 14,
21, 28, and 35 d postmortem). Samples for collagen content, sarcomere length, and western
blotting were obtained at 1 d postmortem. Steer served as the experimental unit for each
dependent variable. Dependent variables were used in multiple linear regression models to
predict loin muscle (LM) WBSF at the aging points of 1, 21, and 35 d postmortem. Dependent
variables included: ADG, stun to chill time interval, LM ultimate pH (UPH), extractable lipid
percentage, fat thickness (FT), lean maturity, skeletal maturity, loin LM area, LM sarcomere
length, LM soluble collagen, LM insoluble collagen, weaning age, birth weight, LM postmortem
temperature at 2, 4, and 8 hrs, and corresponding cook loss percentage for the aging point.
Troponin T and desmin degradation were analyzed on 15 samples (5 steers with the greatest
growth rate, 5 steers closest to the mean growth rate, and 5 steers with the slowest growth rate) at
39
1 and 21 d postmortem. Average daily gain accounted for 36% of the variation in WBSF at 1 d
postmortem (1 d WBSF = 6.38 – (1.42*ADG, kg/d)); however, ADG was not significant at any
other aging point. For prediction of 1 d WBSF using all parameters, a 4 variable equation was
calculated (1 d WBSF = -25.28 + (4.30 * UPH) – (0.29 * extractable lipid, %) + (0.03 * skeletal
maturity) + (0.20 * 1 d cook loss, %), Adj. R2 = 0.72). Whereas, prediction of 21 d WBSF used
a 2 variable model (21 d WBSF = 0.39 + (0.10 * 21 d cook loss, %) + (0.09 * extractable lipid,
%) Adj. R2 = 0.47). Disappearance of the 37 kDa troponin T band accounted for 27% of the
variation in WBSF at 21 d postmortem. Steers were stratified by feedlot ADG into 3 treatment
groups 1.01, 1.50, and 1.96 with mean ADG and standard deviations of 1.01 ± 0.18, 1.50 ± 0.14,
and 1.96 ± 0.09 kg/d, respectively. Treatment group 1 had greater WBSF values (P < 0.05) as
compared with 1.50 and 1.96 at 1 d postmortem. Treatment groups did not differ (P > 0.17) at
any other postmortem aging point. In conclusion, ADG is not the only factor affecting 1 d
WBSF. Also, average daily gain was not able to account for any variation in WBSF at aging
points after 1 d postmortem.
Introduction Palatability of beef is based on consumers’ perception of flavor, juiciness, and tenderness
(Voges et al., 2007). Other research reported tenderness as being the most important trait
associated with consumer satisfaction (Shackelford et al., 2001). Boleman et al. (1997) reported
consumers are willing to pay premiums for beef cuts labeled with a tenderness guarantee. The
United States Department of Agriculture, Agricultural Marketing Service (USDA-AMS) has
proposed tenderness grades for beef to standardize tenderness labeling (USDA-AMS, 2012).
These developments justify a need to classify cattle based on predicted tenderness values.
40
The effect of growth rate on Warner-Bratzler shear force (WBSF) is inconsistent.
Correlations have shown that as average daily gain (ADG) increases, 7 d WBSF decreased
(Shackelford et al., 1994; Smith et al., 2007). In support, others have reported decreased WBSF
in faster growing cattle as compared with slower growing cattle (Aberle et al., 1981; Therkildsen
et al., 2002a, b). However, others have not observed an effect of ADG on WBSF (Burson et al.,
1980; Fishell et al., 1985). Oddy et al. (2001) reported that growth rate has an impact on
collagen crosslinking and proteolytic activity. Other studies have reported that meat tenderness
is determined by sarcomere length and the amount and solubility of collagen (Koohmaraie and
Geesink, 2006). Average daily gain was correlated with calpastatin activity: as ADG increased,
calpastatin activity decreased (Shackelford et al., 1994). Furthermore, an increase in the activity
of calcium dependent protease inhibitors caused decrease postmortem tenderization and an
increase WBSF (Whipple et al., 1990; Shackelford et al., 1994). Increasing ADG could increase
protein turnover by decreasing calpastatin activity and increase postmortem tenderization. Many
researchers have reported ultimate pH (UPH) affecting tenderness (Purchas et al., 1999; Wulf et
al., 2002). However, other studies report the combined effects of pH decline and chill rate have
an impact on tenderness (Hannula and Puolanne, 2004; Hwang and Thompson, 2001). In
addition, multiple postmortem factors may also affect postmortem tenderization; therefore, we
hypothesize that steers with greater ADG will be more tender as compared with cattle with
reduced ADG.
Materials and Methods Steers used during this study were cared for in compliance with the regulations and
guidelines from the University of Illinois Animal Care and Use Committee. Steers were
slaughtered under federal inspection at the University of Illinois Meat Sci. Laboratory.
41
Experimental Design
Thirty-six Angus crossbred steers were obtained from the University of Illinois internal
herd located in Dixon Springs, Illinois. Steers (initial BW=271±24.0 kg) were stratified by
weight and placed into pens of six steers per pen with two pens per treatment for a total of 12
steers/treatment. Data from four steers were excluded from the data set due to the discovery of
testicles during slaughter (two steers from the slow treatment and two from the medium
treatment). Steers were acclimated to pens for approximately four weeks, while dietary
concentrate inclusion was increased to dietary treatment level. Steers were limit fed to target
three different rates of gain (slow, ADG of 1.0 kg/d; medium, ADG of 1.5 kg/d; and fast, ADG
of 1.85 kg/d). Lifetime average daily gain (LTADG) was defined as gain from birth to slaughter
divided by d of age at slaughter. Feedlot average daily gain (FADG) was calculated as gain
during study period divided by d of study period. The study was divided into two parts. In part
one, individual steer (n = 32) served as the experimental unit for determination of predication
equations. In part two, steers were stratified by FADG and grouped into three FADG treatments
1.01 (n = 11), 1.50 (n = 11), and 1.96 (n = 10) with mean ADG and standard deviations of 1.01 ±
0.18, 1.50 ± 0.14, and 1.96 ± 0.09 kg/d, respectively, and individual steer served as the
experimental unit. After 151 days on feed, the two heaviest animals from each pen were
transported to the University of Illinois Meat Sci. Laboratory for slaughter. The next two
heaviest steers in each pen were slaughtered after 161 d on feed with the remaining steers
slaughtered after 167 d on feed. Hot carcass weight (HCW) and time from stunning to initiation
of chill were recoreded.
Diets
Steers were fed diets to meet or exceed NRC requirements. Diets contained (on a DM
basis) 20% corn husklage, 15% dry distillers grain with solubles, 55% cracked corn, and 10%
42
vitamin/mineral supplement containing monensin (Rumensin; Elanco Animal Health, Greenfield,
IN) and tylosin phosphate (Tylan; Elanco Animal Health; Table 1). Diets were limit fed to
achieve 3 different rates of gain. Steers were individually weighed every two weeks and feed
amount was adjusted based on the mean pen weight using NRC (1996) prediction equations for
medium-framed steers to maintain rates of gain.
Chill Rate and Ultimate pH
Carcass chill rate was recorded using temperature recorders (Multitrip, Sensitech, Inc,
Beverly, MA) placed in the center of the longissimus muscle (LM) between the first and second
lumbar vertebra starting at approximately 1.25 hrs postmortem. Temperature measurements
were recorded every 10 min for 23 h after the carcass was placed in cooler. Longissimus muscle
temperature was reported at 2, 4, 8, 12, 18, and 23 hrs postmortem. Ultimate pH of the LM was
measured using a handheld pH meter (pH-star, R. Matthaus, Klausa, Thuringen, Germany) at the
13th rib prior to carcass grading.
Carcass Grading
Carcasses were chilled for approximately 24 hrs prior to grading. Carcasses were ribbed
between the 12th and 13th rib and the ribeye face was allowed to bloom for approximately 20 min
prior to evaluation. Fat thickness was measured at 75% of the distance around the ribeye face
from the spinal dorsal processes. Kidney, pelvic, and heart fat (KPH) was evaluated and
reported as a percentage of HCW. Ribeye area (REA) was determined by tracing the ribeye face
on Dura-Lar film (Grafix, Maple Heights, OH). Traced area was measured in duplicate using a
digitizing tablet (Wacom Co., Ltd; Vancouver, WA). Ribeye area was reported as the average of
the duplicates. Marbling and maturity were evaluated according to the USDA grading standards
(USDA-AMS, 1996).
43
Sample Collection
The right side of each carcass was fabricated following the completion of carcass
grading. Each strip loin was faced prior to being sliced into steaks. The face cut was used for
proximate analysis (lipid and moisture percentage). Aging curves were determined using 6
steaks, 2.54 cm thick, from the rib end of the strip loin (NAMP #180 PSO 4) and randomly
assigned to 1 of 6 aging points (1, 7, 14, 21, 28, or 35 d). Protein degradation samples were
obtained by dividing the seventh steak into 6 sections. Each section was randomly assigned to 1
of the 6 aging points. Aging curve steaks and protein degradation samples were vacuum
packaged and stored at 1°C until assigned aging point. Aging curve steaks and protein
degradation samples were frozen at the completion of aging and stored at -20°C until further
analysis. The eighth steak from the strip loin was used for sarcomere length and collagen
content (soluble and insoluble) determination.
Moisture and Lipid Determination
Steaks were trimmed of subcutaneous fat and homogenized in a food processor (Cuisinart
model CUI DFP-7BC, Cuisinart, East Windsor, NJ) prior to analysis. Proximate composition
was determined as described by Novakofski et al. (1989) in which duplicate 10 g samples were
weighed and dried at 110 °C for 24 hrs to determine moisture content. Dried samples were then
subjected to lipid extraction using an azeotropic mixture of chloroform and methanol.
Warner-Bratzler Shear Force
Steaks were placed in a cooler at 1°C to thaw for 24 hrs. Steaks were trimmed of
excess subcutaneous fat and weighed to determine raw weight. Steaks were cooked on a
Faberware Open Hearth Grill (Model 455N, Walter Kidde, Bronx, NY) until an internal
temperature of 70°C was reached. Internal temperature was monitored using a Digi Sense 12
Channel Scanning Benchtop Thermometer (Model 92000-00, Cole Parmer, Vernon Hills, IL).
44
Steaks were then allowed to cool to approximately 25°C. After cooling, steaks were weighed to
determine cooked weight. Raw and cooked weights were used to calculate percentage cook loss.
Six 1.25 cm cores, parallel to the muscle fibers, were removed from each steak. Cores were
sheared using a Warner-Bratzler attachment with a blade speed of 3.33 mm/sec on a Texture
Analyzer TA HD Plus (Texture Technologies Corp., Scarsdale, NY / Stable Microsystems,
Godalming, UK). The peak shear force value for each of the 6 cores was averaged and reported.
Western Blotting
Sample Selection
Steers were ranked based on individual FADG. The 5 steers with the greatest ADG were
selected for western blotting, along with the 5 steers with the least ADG and the 5 steers closest
to the mean for ADG. Samples from selected steers were analyzed for troponin T and desmin
degradation at 1 and 21 d postmortem.
Whole Muscle Sample Preparation
Samples were prepared using a modified version of the method described by Huff-
Lonergan et al. (1996). Samples were powdered using liquid nitrogen and 100 mg of sample was
weighed into a 2.0 ml microfuge tube. Then, 1.5 ml of whole muscle buffer containing 2%
(wt/vol) SDS and 10 mM sodium phosphate buffer (pH 7.0) was added to each sample tube. The
samples were tissuelyzed at 30 Hz for 90 s and centrifuged for 15 min at 1,500 * g. Protein
concentration of the supernatant was quantified using a BCA Protein Assay Kit (Pierce Protein
Research Products, Rockford, IL). Samples were diluted to a final concentration of 4 mg/ml
with 0.50 ml of Laemmli Sample Buffer (Bio-Rad Laboratories, Hercules, CA), 0.1 ml beta-
mercaptoethanol, and whole muscle buffer. Samples were then heated for 20 min at 50°C.
45
Gel Electrophoresis
Seven microliters (28 µg of protein) of each sample were loaded into either 10%
polyacrylamide gels for desmin or 4 to 20% polyacrylamide gradiant gels for troponin T (Mini-
Protean TGX gel, Bio-Rad Laboratories, Hercules, CA). Gels were electrophoresed in a running
buffer containing 2 mM EDTA, 25 mM Tris, 192 mM glycine, and 1% (wt/vol) SDS for
approximately 90 min at a constant voltage of 90 v. After electrophoresis, gels were transferred
to polyvinylidene difluoride membranes at 0°C for 90 min at a constant voltage of 90 v in
transfer buffer consisting of 25 mM Tris, 192 mM glycine, and 15% methanol (vol/vol).
Blotting
Polyvinylidene difluoride membranes were blocked for 60 min using 0.5% (wt/vol) non-
fat dry milk in PBS Tween. Membranes were incubated either with monoclonal mouse anti-
troponin T (JLT-12, Sigma-Aldrich, St. Louis, MO) or monoclonal mouse anti-desmin (DE-U-
10, Sigma-Aldrich, St. Louis, MO) at a 1:5,000 dilution in PBS Tween at 4°C overnight.
Membranes were washed with PBS Tween 3 times (10 min per wash). Membranes were then
incubated with polyclonal anti-mouse peroxidase antibody (A9044, Sigma-Aldrich, St. Louis,
MO) at a dilution of 1:50,000 for 60 min at 25°C. Membranes were washed 3 more times (10
min per wash) in PBS Tween. Bands were detected using SuperSignal West Pico
Chemiluminescent Substrate (Thermo Scientific, Rockford, IL). Band density was quantified by
densitometry using a ChemiDoc-It 2 Imager (UVP, Upland, CA). Troponin T degradation was
measured by the disappearance of both the 37 and 39 kDa bands (Figure 2) and desmin
degradation was measured by the disappearance of the 56 kDa band (Figure 3). Troponin T and
desmin degradation were reported as ratios to the corresponding band in the reference sample (1
day postmortem beef LM).
46
Sarcomere Length Determination
Sarcomere length was determined on raw steak samples from the LM using helium neon
laser diffraction as described by Cross et al. (1981). Samples were fixed using the method
described by Koolmees et al. (1986); in which, samples are fixed in a solution containing 5%
glutaraldehyde and 0.1 M sodium phosphate buffer at a pH of 7.2 and chilled at 4 °C for 4 hrs.
Samples were then washed in a 0.2 M sucrose solution containing 0.1 M sodium phosphate
buffer at a pH of 7.2. Samples remained at 4°C in the wash solution for a minimum of 18 h.
From each sample, 4 sub-samples were collected and placed on a glass microscope slide. From
each sub-sample, 5 sets of diffraction bands were traced on a sheet of paper (20 total
measurements per sample) using helium neon laser diffraction (Model 05-LHR-021, Melles
Griot, Carlsbad, CA). Sarcomere length was determined by the equation:
𝑆𝑎𝑟𝑐𝑜𝑚𝑒𝑟𝑒 𝑙𝑒𝑛𝑔𝑡ℎ (𝜇𝑚) =632.8 × 10−3 ∗ 𝐷 ∗ √(
𝑇𝐷)
2
+ 1
2 ∗ 𝑇
where, D is the distance from the microscope slide to the paper (D was held constant a 100 mm)
and 2*T is the distance between the diffraction bands (Cross et al., 1981). The mean of the 20
sarcomere lengths was reported as the sarcomere length of the sample.
Collagen Content
Collagen content was determined by acid hydrolysis on a raw steak from the LM taken
posterior to the WBSF samples. Soluble and insoluble collagen were separated using a modified
version of the Hill (1966) procedure. Samples were powdered in liquid nitrogen and weighed in
duplicates into 50 ml polyethylene centrifuge tubes containing 16 mg of ¼ strength Ringer’s
solution. Samples were incubated in a water bath at 77°C for 70 min. Samples were then
centrifuged at 5200 x g for 10 min. The supernatant was decanted from the centrifuge tube into a
47
glass screw top test tube and labeled as soluble fraction. Acid hydrolysis was performed using
the procedure described by Cross et al. (1973), in which each fraction was heated at 110°C and 1
atm pressure in the presence of 6N HCl acid for a minimum of 6 h. Hydroxyproline content was
determined as described by Bergman and Loxley (1963); modified to use a 96 well micro plate
reader. After acid hydrolysis, samples were allowed to cool to room temperature and were
decolorized using 1 g of charcoal powder. The pH of both soluble and insoluble fractions was
adjusted to 6.0 and fractions were then volumized to either 250 ml for soluble and 500 ml for
insoluble. One ml of each sample was pipetted into a screw top test tube along with 2 ml of
isopropanol. One ml of the oxidant solution containing 7% (weight/volume) Chloramine T
dissolved in acetate/citrate buffer was pipetted into each tube. Four min after the oxidant
solution was added to each tube, 4 ml of Elrich’s reagent was added to each tube. Tubes were
then capped and placed in a water bath at 60°C for 25 min. After incubation, sample absorbance
was read at 558 nm in a 96 well micro plate reader. Collagen content was calculated as
𝑚𝑔 𝑐𝑜𝑙𝑙𝑎𝑔𝑒𝑛 𝑝𝑒𝑟 𝑔 𝑜𝑓 𝑚𝑒𝑎𝑡 = (𝑚𝑔𝑚𝑙
𝑜𝑓 ℎ𝑦𝑑𝑜𝑥𝑦𝑝𝑟𝑜𝑙𝑖𝑛𝑒) ∗ 𝑑 ∗ 𝑐
𝑤 ∗ 1000
where, d is the dilution factor, c is the constant (7.52 for soluble and 7.25 for insoluble), w is the
sample weight, and mg/ml of hydroxyproline is derived from the absorbance value of the micro
plate (Cross et al., 1973). Collagen content was reported as mg/g of meat tissue.
Statistical Analysis
In part one, individual steer (n = 32) served as the experimental unit for all dependent
variables. Pearson correlation coefficients were calculated using PROC CORR in SAS (SAS
Inst. Inc., Cary, NC). Correlations were considered significant at an alpha ≤ 0.05. Regression
functions were analyzed using PROC REG. The STEPWISE selection method, alpha = 0.15 for
48
entry into model, Mallow’s Cp, and Akaike information criterion (AIC) were used to select the
optimum prediction equations for WBSF at 1, 21, and 35 d postmortem. The STEPWISE
selection method was also used to evaluate proteolysis models at 1 and 21 d postmortem. The
VIF option was used to analyze for multicollinearity among the predictor variables. Excess
multicollinearity was indicated by VIF values greater than 10 and all prediction variables with
VIF > 10 were removed prior to STEPWISE selection. Influential points were identified using
DFFITS and DFBETAS. A point was considered influential if the DFFIT or DFBETA values
were greater than 1.
In part two, steers FADG treatments 1.01, 1.50, and 1.96 were analyzed using a one-way
ANOVA. Individual steer served as the experimental unit (n = 11 for 1.01 and 1.50 and n = 10
for 1.96). Data were analyzed using the MIXED procedure in SAS (SAS Inst. Inc., Cary, NC).
Feedlot average daily gain treatment served as the fixed effect in the model. Treatment means
were calculated using the LSMEANS option and separated using the PDIFF function.
Treatments were considered different at an alpha ≤ 0.05 and trending at an alpha between 0.1 and
0.05.
Results and Discussion
Growth Performance and Carcass Characteristics
Population growth performance and carcass characteristic data are presented in Table 2.
The range in values for feedlot ADG and WBSF were 0.75 to 2.13 kg/d and 2.75 to 6.60 kg at 1
d postmortem, respectively. The range in ADG was dictated by the experimental design of this
study. The mean (± SD) HCW, LM area, KPH, fat thickness (FT), marbling score, lean
maturity, skeletal maturity, and overall maturity were 296 (± 44) kg, 74.4 (± 8.9) cm2, 1.76 (±
0.60)%, 0.60 (± 0.37) cm, 542 (± 92), 150 (± 11), 153 (± 11), 152 (± 8), respectively. In
49
comparison to the population of cattle represented in the 2011 beef quality audit (Moore et al.,
2012), steers in this experiment had similar maturity scores, reduced HCW, LM area, KPH, and
FT, but greater marbling scores. Steers in this population were expected to have reduced HCW,
LM area, and FT because of the study design. Warner-Bratzler shear force values at 21 and 35
days postmortem were 2.87 (±0.52) kg and 2.73 (±0.50) kg, respectively (Table 3). Carcasses in
this study at 21 d postmortem had 0.24 kg greater WBSF as compared with the 22 d postmortem
WBSF reported by the 2013 National Beef Tenderness Survey (Guelker et al., 2013).
Laboratory Analysis
Population summary statistics for laboratory analyzed data are presented in Table 4.
Mean sarcomere length was 1.74 ± 0.14 µm with a minimum length of 1.31 µm and a maximum
of 2.00 µm. The mean and maximum sarcomere length are in agreement with other published
data (mean 1.77 ± 0.08 µm and maximum 2.01 µm) on sarcomere length of beef LM; however,
the minimum sarcomere length in this study is less than the minimum length (1.68 µm) reported
in other studies (Wheeler et al., 2002). The minimum sarcomere length of 1.30 µm is similar to
the sarcomere lengths reported in cold shortened longissimus steaks (King et al., 2002). The
shorter sarcomere lengths may indicate cold shortening of the sarcomeres in the LM. Total
collagen content was 4.45 mg/g fresh tissue with 11.84 % being soluble. Collagen content was
similar to other studies. Herring et al. (1967) reported similar values for total collagen content
(4.52 mg/g) in the LM of A maturity cattle; however, the percent soluble collagen (10.48 %) was
less. Other authors reported that concentrate finished Angus-cross steers had total collagen
content of 5.34 mg/g in LM with 10.92 % soluble (Duckett et al., 2007).
Variable Selection and Analysis of Influential Points
All measured and calculated variables (ADG, stun to chill interval, ending live weight,
HCW, KPH, marbling score, UPH, extractable lipid, FT, lean maturity, skeletal maturity, overall
50
maturity, USDA yield grade, LM area, sarcomere length, soluble collagen, insoluble collagen,
total collagen, weaning age, birth weight, postmortem temperatures (2, 4, 8, 12, 18, and 23 hrs),
and corresponding cook loss percentage for the aging point) were analyzed for multicollinearity.
Pearson correlation coefficients for carcass and growth measurements are presented in Table 5.
Collinearity was expected between ADG, HCW, and ending live weight since these variables are
highly correlated (r > 0.56; Reinhardt et al., 2012). Collinearity was also expected between total
collagen and soluble and insoluble collagen, because total collagen is the sum of soluble and
insoluble collagen (Table 6). Variables with variation inflation factors (VIF) values greater than
10 (ending live weight, HCW, KPH, marbling score, overall maturity, USDA yield grade, total
collagen, postmortem temperatures at 4, 12, and 18 hrs) were not used as candidate variables.
Seventeen remaining candidate variables were used in the prediction of tenderness at 1, 21, and
35 d postmortem. Using the DFBETA criterion, three steers in the slow rate of ADG treatment
were considered influential points based on DFBETA value for one day WBSF; therefore, data
for the steers were removed for 1 d WBSF analysis. For the WBSF prediction equation at 21 d,
the DFBETA value for UPH for a steer in the slow treatment group exceeded the criterion for an
influential point and the data were removed prior to analysis. No points were considered
influential in prediction of 35 d WBSF.
Pearson Correlation Coefficients and Shear Force Prediction Equations
Pearson correlation coefficients for growth and carcass parameters are presented in Table
5. Average daily gain was correlated with ending live weight (r = 0.87, P < 0.01) and HCW (r =
0.88, P < 0.01). Correlations between ADG and both ending live weight and HCW were
expected. Ending live weight and HCW are dependent on initial BW, ADG, and the number of
days cattle are on feed. Therefore, ADG will be correlated with ending live weight and HCW
51
when cattle are similar in initial BW (271 ± 24 kg), slaughtered at an equal number days on feed,
and ADG is varied.
Correlations between ADG and laboratory analysis are presented in Table 6. Extractable
lipid and ADG were positively correlated (r = 0.75, P < 0.01). The correlation between
extractable lipid and ADG was expected, since the faster growing steers were slaughtered at a
typical finished weight. Studies have reported that as cattle approach mature weights (25%
empty body fat), protein mass increases at a decreasing rate; whereas, fat mass increases at an
increasing rate (Owens et al., 1995). Owens et al. (1995) also indicated that feed restriction in
cattle resulted in reduced fat mass. Because intramuscular fat depots develop lastly, studies
indicate that cattle would have a USDA quality grade of low Choice at 28% empty body fat (Fox
et al., 1992). Other studies also reported an increase from low select to low choice in USDA
quality grade between pasture finished (ADG 0.85 kg/d) and concentrate finished (ADG 1.23
kg/d) Angus-cross steers slaughtered at equal ages (Duckett et al., 2007; Neel et al., 2007). Other
authors reported an increase in USDA quality grade from low standard to low choice when
Herford x Angus cross steers harvested at equal ages were fed for an ADG of 0.34 kg/d
compared with steers fed for an ADG of 1.42 kg/d (Fishell et al., 1985). Average daily gain was
not correlated with percentage soluble collagen (r = -0.01, P = 0.97) or percentage insoluble
collagen (r = 0.01, P = 0.97) content. This finding is consistent with another study reporting no
difference in percentage soluble or percentage insoluble collagen in the LM between fast and
slow growing cattle (Duckett et al., 2007).
Correlations between predictor variables and 1, 21, and 35 d postmortem shear force are
reported in Table 7. Extractable lipid percentage and marbling score strongly correlated (r =
0.86, P < 0.01). Marbling score was moderately correlated with 1 d WBSF (r = -0.50, P < 0.01)
52
and extractable lipid percentage (r = -0.54, P < 0.01). Shackelford et al. (1991) reported similar
correlations of -0.50 and -0.51 (P < 0.05) between WBSF and marbling score at 1 and 3 days
postmortem; however, no correlations (P > 0.05) between WBSF and marbling score were
reported at 7 or 14 d postmortem. Other studies have reported reduced correlations between
marbling score and 14 d WBSF (r < -0.33; Platter et al., 2003; Nelson et al., 2004). The
correlations between marbling score and early postmortem aging points indicated that marbling
score has an effect on WBSF only during early aging times. Average daily gain was correlated
with 1 d WBSF (r = -0.53, P < 0.01) along with FT (r = -0.58, P < 0.01) and LM area (r = -0.47,
P < 0.01). It is expected that if ADG is correlated with 1 d WBSF then, FT and LM should also
be correlated because faster growing cattle should have larger LM areas and greater FT when
slaughtered at equal ages. However, ADG was not correlated with WBSF at any other aging
point (P > 0.24). Other studies have reported no difference in WBSF in cattle with different
ADG (P > 0.05; Burson et al., 1980; Fishell et al., 1985). In contrast, other studies have reported
that ADG was correlated with 7 day WBSF (Shackelford et al., 1994; Smith et al., 2007). Aberle
et al. (1981) reported a 1.84 kg difference in WBSF for cattle with a growth rate of 0.70 and 1.34
kg/d. The conflicting effects of ADG on WBSF may indicate other factors are involved in
determining WBSF.
Prediction Models
An objective of the study was to characterize the effect of ADG on WBSF. As ADG
increased by 1 kg/d day, 1 d WBSF decreased (P < 0.01) by 1.42 kg/d (Adj R2 = 0.36; Figure 4).
However, other variables were able to account for more variation in the model using STEPWISE
selection. Two prediction models were calculated for each aging time point. The carcass
parameter model consisted of ADG, stun to chill time, UPH, extractable lipid, lean maturity,
bone maturity, LM area, weaning age, birth weight and, postmortem temperature at 2, 8, and 23
53
hrs. The complete model used all candidate variables in the development of a tenderness
prediction model. The best carcass parameter model to predict 1 d WBSF was a 3 variable
equation (1 d WBSF = – 8 .10 – (0.97 * FT, cm) – (0.036 * LM area, cm2) + (2.87 * UPH) Adj.
R2 = 0.41). However, the best all-inclusive model equation accounted for 72 % of the variation
in 1 d WBSF (1 d WBSF = -25.28 + (4.30 * UPH) – (0.29 * extractable lipid, %) + (0.03 * bone
maturity) + (0.20 * 1 d cook loss, %), Adj. R2 = 0.72). Ultimate pH accounted for WBSF
variation in both models. The positive coefficient for UPH agrees with other studies reporting
that an increased UPH has been associated with increased WBSF (Purchas et al. 1999; Wulf et
al. 2002). The lack of collagen content or collagen solubility to account for variation in the
model is consistent with other studies reporting that collagen does not account for variation in
tenderness of youthful concentrate-fed cattle (Koohmaraie et al., 1995). Shackelford et al. (1991)
reported an R2 = 0.85 when using a five variable (cystatin activity at 24 hrs postmortem, calcium
dependent protease inhibitor at 24 hrs postmortem, calcium dependent protease II at 24 hrs
postmortem, internal LM temperature at 12 hrs postmortem, and pH at 9 hrs postmortem) to
predict 1 day WBSF. The proteolytic enzymes may have a greater effect on 1 d WBSF as
compared with carcass parameters. No significant carcass parameter model existed to predict 21
d WBSF (P > 0.15); therefore, the best complete model to predict 21 d WBSF used 2 variables
(21 d WBSF = 0.39 + (0.10 * 21 d cook loss, %) + (0.09 * extractable lipid, %) Adj. R2 = 0.47).
Stun to chill time accounted for 11 % of the variation in 35 d WBSF (35 d WBSF = 1.22 +
(0.020 * stun to chill time, min) Adj. R2 = 0.11). The effect of stun to chill time on 35 d WBSF
samples could be caused by increased protein denaturation due to delayed chilling.
Proteolysis
Population statistics for proteolysis ratios to reference values are presented in Table 8.
Correlations between 1 or 21 d WBSF and corresponding 37 kDa troponin T, 39 kDa troponin T,
54
and 56 kDa desmin values are presented in Table 9. A significant correlation (r = 0.61) existed
between 39 kDa troponin T and 1 d WBSF indicating positive relation. Disappearance of the 39
kDa troponin T accounted for 31.4 % of the variation in 1 d WBSF (1 d WBSF = 1.14 + (4.61 *
39 kDa troponin T, ratio to reference) Adj. R2 = 0.31; Figure 5). The reduction in WBSF as the
amount of intact 39 kDa troponin T decreased (P = 0.02) is indicative of protein degradation
causing a change in WBSF. Degradation of troponin T has been reported to be strongly
correlated with beef tenderness (Montgomery et al., 2000). Koohmaraie (1996) reported that the
variation in tenderness of young grain fed cattle was due to the rate of postmortem proteolysis,
indicating that some cattle are considered tender by 1 d postmortem and others need a longer
aging period. Disappearance of the 37 kDa troponin T explained 26.8 % of the variation at 21 d
postmortem (21 d WBSF = 4.05 – (1.38*37 kDa troponin T, ratio to reference) Adj. R2 = 0.27)
(Figure 6). The negative coefficient was not expected because decrease ratio to reference values
for 37 kDa troponin T should be associated with decreased WBSF. No correlation was reported
(P > 0.5) between ADG and 37 kDa troponin T, 39 kDa troponin T, or desmin at either 1 or 21 d
postmortem (Table 10).
One-way ANOVA analysis
Carcass parameter least square means for feedlot average daily gain (FADG) treatments
are presented in Table 11. Ending live weight, HCW, marbling score, extractable lipid, and
USDA quality grade were the greatest in cattle fed to gain 1.96 kg/d. The greater weights and
quality grades were expected in the 1.96 kg/d treatment. Fishell et al. (1985) also reported faster
growing cattle had greater HCW, marbling score and USDA quality grades. Fat thickness, KPH,
and USDA yield grade did not differ (P ≥ 0.06) between 1.50 and 1.96; however, 1.01 had
reduced (P < 0.01) FT, KPH, and USDA yield grade as compared with 1.50 and 1.96. The
reduced FT and KPH were expected since 1.01 steers were the least finished of the treatment
55
groups. Quality grade was expected to be lower in 1.01 since the steers had reduced fat
thickness and reduced KPH. Lean maturity color scores were greater (P = 0.02) in 1.01 as
compared with 1.50 and 1.96. Two steers in 1.01 were classified as dark cutters. The darker
lean maturity color score and dark cutters may indicate lower muscle glycogen stores. Ultimate
pH of 1.01 was trending (P = 0.07) to be greater as compared with 1.50 and 1.96. The increased
UPH supports the increased lean maturity color scores being caused by reduced glycogen stores.
Temperature decline data for the LM is presented in Figure 7. The longissmus temperature for
1.01 was less (P ≤ 0.01) than 1.50 and 1.96 at 2, 4, 8, 12, and 18 hours postmortem. At 23 h
postmortem, 1.01 was different (P < 0.01) from 1.96 and trended to be different (P = 0.06) from
1.50. The difference in temperature at each postmortem time point was likely caused by the
reduced FT, HCW, and LM area of 1.01.
Collagen content and sarcomere length data are presented in Table 12. Total collagen
content, percent soluble collagen, and percent insoluble collagen did not differ (P ≥ 0.29) among
treatments. Fishell et al. (1985) reported no difference in collagen content of the LM of cattle
grown at different rates of gain Sarcomere length did not differ (P = 0.13) among treatments.
Cook loss percentage is presented in Figure 8. Cook loss percentage was not different (P
≥ 0.18) at any postmortem aging point. Warner-Bratzler shear force data is presented in Figure
9. Warner-Bratzler shear force was greater (P = 0.05) for 1.01 as compared with 1.50 and 1.96
at the one day postmortem aging point. Warner-Bratzler shear force was not different (P ≥ 0.17)
at any other postmortem aging point. Fishell et al. (1985) also reported no difference in WBSF
at 7 days postmortem. Other studies reported that 1 d WBSF > 6.00 kg resulted in greater WBSF
at other postmortem aging points as compared with 1 d WBSF < 6.00 kg (Koohmaraie et al.,
56
1995). This data supports the lack of difference at aging points past 1 d postmortem. Average
daily gain may only affect WBSF during postmortem aging times of less than 7 d.
Conclusion Further investigation should focus on the effects of ADG on biological processes that
regulate protein synthesis and protein degradation and the relations to early postmortem
tenderness. The results of this study demonstrate an effect of ADG on 1 d WBSF; however, only
36% of the variation was accounted for by ADG. Prediction of WBSF using measured carcass
parameters may allow for carcasses with the potential reduced WBSF to be identified and
segregated into tenderness classifications. For beef producers, being able to sort carcasses into
predicted tenderness categories could reduce the variation in tenderness in beef products. The
reduction in tenderness variation in beef products could increase consumer preference.
57
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60
Table 1. Diet composition, fed during the finishing phase of the experiment.
Ingredient % of Diet, DM Basis
Cracked corn 55
Dry distillers grains with solubles 15
Corn husklage 20
Supplement
Ground corn 5.488
Limestone 2.689
Urea 1.614
Dairy TM salta 0.108
Fat 0.0718
Rumensin 90 0.0183
Tylan 40 0.0118
a Dairy trace mineral salt contains: 8.5% Ca (as CaCO3), 5% Mg (as MgO and MgSO4),
7.6% K (as KCl2), 6.7% Cl (as KCl2) 10% S (as S8, prilled), 0.5% Cu (as CuSO4 and
Availa-4 (Zinpro Performance Minerals; Zinpro Corp, Eden Prairie, MN)), 2% Fe (as
FeSO4), 3% Mn (as MnSO4 and Availa-4), 3% Zn (as ZnSO4 and Availa-4), 278 ppm
Co (as Availa-4), 250 ppm I (as Ca(IO3)2), 150 Se (Na2SeO3), 2,205 KIU/kg VitA (as
retinyl acetate), 662.5 KIU/kg VitD (as cholecalciferol), 22,047.5 IU/kg VitE (as DL-α-
tocopheryl acetate), and less than 1% CP, fat, crude fiber, salt.
61
Table 2. Population summary statistics of growth performance and carcass
characteristics.
Item Mean Minimum Maximum
Standard
Deviation N
Growth Performance
Birth weight, kg 37.3 24.9 52.5 6.8 31
Weaning age, d 68.9 38 110 18.9 32
ADG life, kg/d 0.82 0.62 1.01 0.12 32
ADG feed yard, kg/d 1.48 0.75 2.13 0.42 32
Ending live weight, kg 483 384 576 62 32
Carcass Characteristics
Ultimate pH 5.48 5.36 5.73 0.09 32
Fat thickness, cm 0.60 .13 1.65 0.37 32
KPH, % 1.77 0.50 2.50 0.60 32
LM area, cm2 74.3 58.8 91.4 8.9 32
USDA yield grade 2.24 1.44 3.48 0.54 32
Marbling scorea 541 290 700 92.2 32
Extractable lipid, % 5.40 2.29 9.06 1.73 32
Bone maturityb 153 140 180 10.6 32
Lean maturityb 152 140 180 10.8 31
Overall maturityb 152 140 170 8.0 31
a200 = Practically devoid00, 300 = Traces00, 400 = Slight00, 500 = Small00, 600 =
Modest00, and Moderate = 70000
b100 = A00 and 200 = B00
62
Table 3. Population summary statistics of Warner-Bratzler shear force (WBSF) and
cook loss data
Item Mean Minimum Maximum
Standard
Deviation N
WBSF
1 d postmortem, kg 4.35 2.75 6.60 1.00 32
7 d postmortem, kg 3.46 2.17 5.47 0.82 32
14 d postmortem, kg 3.33 2.28 5.38 0.67 32
21 d postmortem, kg 2.87 2.08 4.27 0.52 32
28 d postmortem, kg 2.84 2.02 4.64 0.59 32
35 d postmortem, kg 2.73 1.88 3.63 0.50 32
Cook lossa
1 d postmortem, % 18.14 11.86 25.86 3.26 32
7 d postmortem, % 19.15 12.27 27.24 3.70 32
14 d postmortem, % 19.70 12.30 27.70 3.73 32
21 d postmortem, % 19.63 11.96 27.07 3.56 32
28 d postmortem, % 20.53 10.99 30.88 4.64 32
35 d postmortem, % 17.65 10.25 26.36 3.99 32
a Cook loss calculated as: % cook loss = (raw weight - cook weight) / raw weight) * 100
63
Table 4. Population summary statistics of temperature decline and laboratory analysis
Item Mean Minimum Maximum
Standard
Deviation N
Stun to chill time, min 76.1 58.0 99.0 9.43 32
Temperature Decline
2 hrs postmortem, °C 35.3 30.7 37.6 1.71 32
4 hrs postmortem, °C 24.9 19.5 27.9 2.43 32
8 hrs postmortem, °C 15.0 9.0 18.0 2.18 32
12 hrs postmortem, °C 10.4 6.4 12.9 1.68 32
18 hrs postmortem, °C 6.4 4.2 8.8 1.22 32
23 hrs postmortem, °C 4.6 2.7 6.8 1.07 32
Laboratory Analysis
Sarcomere length, µm 1.74 1.31 2.00 0.14 32
Soluble collagen, % 11.84 6.17 26.74 4.32 32
Insoluble collagen, % 88.16 73.26 93.83 4.32 32
Total collagena, mg/g 4.45 3.08 5.56 0.69 32
a Total collagen calculated as: total collagen = soluble collagen + insoluble collagen
64
Table 5. Pearson correlation coefficients among growth performance and carcass characteristics
Item Live weight Hot carcass weight Fat thickness Kidney, pelvic, &
heart fat
Loin
muscle
area
USDA yield
grade
Average daily gain 0.87* 0.88* 0.55* 0.76* 0.78* 0.49*
Live weight
0.99* 0.60* 0.64* 0.85* 0.54*
Hot carcass weight
0.58* 0.63* 0.85* 0.54*
Fat thickness
0.57* 0.38* 0.89*
Kidney, pelvic, & heart fat
0.51* 0.60*
Loin muscle area
0.14
USDA yield grade
* indicates that the r is significant (P < 0.05)
65
Table 6. Pearson correlation coefficients between average daily gain and carcass
parameters and laboratory analysis
Item Average Daily Gain
Ultimate pH 0.30
Extractable Lipid 0.75*
Percent Insoluble collagen -0.01
Percent Soluble collagen 0.01
Total collagen -0.20
Sarcomere length -0.21
* indicates that the r is significant (P < 0.05)
66
Table 7. Correlation coefficients among prediction variables and 1, 21, and 35 d
postmortem shear force
Warner-Bratzler Shear Force
Item 1 d postmortem 21 d postmortem 35 d postmortem
Birth Weight -0.14 -0.16 0.04
Weaning age 0.05 0.06 0.09
ADG feed yard -0.53* 0.16 0.08
Fat thickness -0.58* 0.23 0.09
LM area -0.47* 0.004 0.09
Extractable lipid -0.54* 0.21 0.04
Bone maturity -0.07 0.24 -0.12
Lean maturity 0.40 -0.12 -0.13
Stun to chill time 0.05 0.23 -0.06
2 hrs postmortem -0.44* 0.09 0.11
8 hrs postmortem -0.56* 0.04 -0.08
23 hrs postmortem -0.41* 0.21 0.25
Sarcomere length -0.07 -0.24 -0.21
Soluble collagen -0.02 -0.03 0.02
Insoluble collagen -0.13 -0.10 -0.05
Ultimate pH 0.40* -0.06 0.09
1 d postmortem cook loss 0.61*
21 d postmortem cook loss 0.65*
35 d postmortem cook loss
0.12
* indicates that the r is significant (P < 0.05)
67
Table 8. Population summary statistics of proteolysis data
Item Mean Minimum Maximum
Standard
Deviation N
1 d Warner-Bratzler Shear
Force
37 kDa Troponin Ta 0.93 0.65 1.31 0.18 15
39 kDa Troponin Ta 0.73 0.56 1.00 0.14 14
56 kDa Desmina 0.96 0.27 1.35 0.26 14
21 d Warner-Bratzler Shear
Force
37 kDa Troponin Ta 0.83 0.58 1.13 0.18 15
39 kDa Troponin Ta 0.22 0.08 0.33 0.07 14
56 kDa Desmina 0.18 0.07 0.56 0.07 15
a Ratio to Reference
68
Table 9. Correlation coefficients among proteolysis ratios and 1 or 21 d postmortem shear
force
Warner-Bratzler Shear Force
Item 1 d postmortem 21 d postmortem
37 kDa Troponin T -0.06 -0.57*
39 kDa Troponin T 0.61* 0.25
56 kDa Desmin 0.35 0.12
* indicates that the r is significant (P < 0.05)
69
Table 10. Pearson correlation coefficients between average daily gain and
proteolysis ratio
Item Average Daily Gain
1 d Postmortem
37 kDa Troponin T 0.30
39 kDa Troponin T -0.37
56 kDa Desmin 0.18
21 d Postmortem
37 kDa Troponin T -0.44
39 kDa Troponin T -0.30
56 kDa Desmin 0.12
* indicates that the r is significant (P < 0.05)
70
Table 11. Carcass parameter least square means for steers fed for different rates of gain.
Average daily gain, kg/d
Item 1.01 1.50 1.96 SEMw P-value
Ending live weight, kg 419a 493b 542c 12 < 0.01
Hot carcass weight, kg 252a 304b 338c 8 < 0.01
Dressing percentage, % 60.0a 61.5b 62.3b 0.5 < 0.01
Ultimate pH 5.52 5.43 5.49 0.03 0.07
Fat thickness, cm 0.31a 0.69b 0.83b 0.10 < 0.01
KPH, % 1.18a 1.91b 2.25b 0.13 < 0.01
LM area, cm2 67.9a 73.0a 83.3b 2.0 < 0.01
USDA yield grade 1.79a 2.49b 2.46b 0.14 < 0.01
Marbling scorex 459a 544b 631c 19 < 0.01
Extractable lipid, % 3.94a 5.45b 6.95c 0.39 < 0.01
Skeletal maturityy 147a 158b 154ab 3 0.05
Lean maturityy 157a 146b 146b 3 0.03
Overall maturityy 152 153 152 3 0.97
USDA quality gradez 4 5 6 < 0.01
a,b,c Means within a row of experimental treatment with different superscripts differ (P <
0.05).
w Pooled SE of treatment means; n= 10 to 11 steers/treatment. Because of unequal
numbers among treatments, the largest SE is reported.
x300 = Traces00, 400 = Slight00, 500 = Small00 and, 600 = Modest00,
y100 = A00 and 200 = B00
z 4 = select, 5 = low choice, 6 = average choice
71
Table 12. Laboratory analyzed parameter least square means for steer fed for different rates
of gain
Average daily gain, kg/d
Item
1.01
1.50
1.96 SEM1
P-value
Soluble collagen1, % 12.4 11.2 11.9 1.4 0.83
Insoluble collagen2, % 87.6 88.8 88.1 1.4 0.83
Total collagen, mg/g 4.62 4.54 4.16 0.22 0.29
Sarcomere length, µm 1.76 1.78 1.66 0.04 0.13
a,b,c Means within a row of experimental treatment with different superscripts differ (P <
0.05).
1 Pooled SE of treatment means; n= 10 to 11 steers/treatment. Because of
unequal numbers among treatments, the largest SE is reported.
2 Expressed as a percentage of the total amount of collagen.
72
Figure 2. Western blots of troponin T at 1 day postmortem (A) and 21 day postmortem (B). Lane MWS is the molecular weight
standard. Lane STD is the 1 day postmortem internal standard. The 39 kDa intact troponin T band decreased in all growth treatments
from 1 day to 21 days postmortem. The 30 kDa degradation band was not present at 1 day postmortem but was present at 21 days
postmortem.
73
Figure 3. Western blots of desmin at 1 day postmortem (A) and 21 days postmortem (B). Lane MWS is the molecular weight
standard. Lane STD is the 1 day postmortem internal standard. The 56 kDa intact desmin band decreased as rate of gain increased.
The 38 kDa degradation band was not present at 1 day postmortem but was present at 21 days postmortem.
A B
74
Figure 4. The effect of average daily gain on 1 d Warner-Bratzler shear force. Average daily
gain was able to account for 36% of the variation in 1 d Warner-Bratzler shear force. For every
kg/day increase in average daily gain, Warner-Bratzler shear force decreased by 1.42 kg. The
legend indicates the treatment pens of the steers.
0.5 1.0 1.5 2.0 2.50
2
4
6
8Slow
Medium
Fast
0.01 P 0.36 R² Adj
6.38 1.42x - y
Average Daily Gain (kg/day)
Warner-BratzlerShear Force (kg)
75
Figure 5. The effect of the 39 kDa troponin T ratio to reference value on 1 d Warner-Bratzler
shear force. Disappearance of the 39 kDa troponin T band accounted for 31% of the variation in
1 d Warner-Bratzler shear force. The legend indicates the treatment pens of the steers.
0.4 0.6 0.8 1.0 1.20
2
4
6
8Slow
Medium
Fast
0.02 P 0.31 R² Adj
61.4 1.14x y
Ratio to Reference
Warner-BratzlerShear Force (kg)
76
Figure 6. The effect of the 37 kDa troponin T ratio to reference value on 21 d Warner-Bratzler
shear force. Disappearance of the 37 kDa troponin T band accounted for 27% of the variation in
21 d Warner-Bratzler shear force. The legend indicates the treatment pens of the steers.
0.0 0.5 1.0 1.51.5
2.0
2.5
3.0
3.5
4.0Slow
Medium
Fast
0.03 P 0.27 R² Adj
05.438.1- y
x
Ratio to Reference
Warner-BratzlerShear Force (kg)
77
Figure 7. Internal temperature of the longissmus lumborum during chilling. The internal
temperature of the longissmus was lower (P < 0.01) in the 1.01 kg/d treatment compared
with the 1.50 kg/d and 1.96 kg/d treatments at 2, 4, 6, 12, and 18 hour postmortem time
points. The internal temperature of the longissimus was higher (P < 0.01) in the 1.96
kg/d treatment as compared with the 1.01 kg/d at 23 hours postmortem. The legend
indicates the treatment ADG of the steers for the ANOVA analysis.
0
5
10
15
20
25
30
35
40
2 4 8 12 18 23
Internal
Temperature of
the Longissmus
(°C)
Time Postmortem (hours)
1.01 kg/d
1.50 kg/d
1.96 kg/d
*
*
*
*
**
78
Figure 8. Cook loss percentage at each given aging point. Cook loss precentage among
treatments did not differ (P ≥ 0.18) at any given aging point. The legend indicates the
treatment ADG of the steers for the ANOVA analysis.
0
5
10
15
20
25
1 7 14 21 28 35
Cook loss
Percentage (%)
Aging Point (d)
1.01 kg/d
1.50 kg/d
1.96 kg/d
79
Figure 9. Warner-Bratzler shear force at each aging point. Warner-Bratzler shear force
was greater (P = 0.05) for the 1.01 kg/d treatment compared with the 1.50 and 1.96 kg/d
treatments at one day postmortem. Warner-Bratzler shear force did not differ (P > 0.17)
at any other aging point. The legend indicates the treatment ADG of the steers for the
ANOVA analysis.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
1 7 14 21 28 35
Warner-Bratzler
Shear Force (kg)
Aging Point (d)
1.01 kg/d
1.50 kg/d
1.96 kg/d
*