Project Summary

A Study to Investigate the Contribution of Different Tenderness Components to Individual Beef Muscle Tenderness in 8 Major Beef Muscles

Principle Investigator(s):
Peang A. Hammond, Colin K.Y. Chun, Amelia A. Welter, Travis G. O’Quinn, Geraldine Magnin-Bissel, Erika Geisbrecht, and Michael D. Chao
Kansas State University
Completion Date:
March 2021

Tenderness encompasses a universal term describing the amount of force required to bite through a piece of meat.  Three different factors underlie the complexity of tenderness: 1) the actomyosin effect or the influence of muscle fibers; 2) the background effect or the influence of connective tissue; and 3) the bulk density or lubrication effect, or the tenderness contributed by intramuscular fat. Each of the factors listed above is further influenced by a multitude of different components contributing to the overall meat tenderness. 

 Countless studies over the past 3 decades have evaluated the impact of various individual tenderness contribution components on meat tenderness, to name a few: proteolysis, sarcomere length, fat content, collagen content and collagen crosslinks. However, the overall perception of beef tenderness is dependent on all the tenderness components as well as the interaction among them and evaluating 1 or 2 tenderness component does not provide the whole picture. 

 In a beef tenderness ranking study supported by Beef Checkoff, longissimus thoracis (ribeye roll; LT), rectus femoris (knuckle; RF), rectus abdominis (flank; RA) and tricep brachii (shoulder clod; TB) were categorized in the intermediate tenderness group, and supraspinatus (chuck tender; SS), semitendinosus (eye of round; ST), pectoralis profundus (brisket; PP), and gluteus medius (top sirloin butt; GM) were categorized as tough. These muscles within each tenderness category are the perfect test subjects for this exploratory project as these muscles have different functions in animals and are known to have different sensory characteristics.


Ten USDA upper 2/3 choice beef carcasses at one day postmortem were selected from a Midwest beef packing plant. Brisket (NAMP #120; figure 1), shoulder clod (NAMP #114; figure 2), ribeye roll (NAMP #112; figure 4), top sirloin butt (NAMP #184; figure 6), knuckle (NAMP #167; figure 7), and eye of round (NAMP #171C; figure 8) were collected only from the left side of the carcass. The flanks (NAMP#193; figure 6) and chuck tenders (NAMP #116B; figure 3) were collected from both sides of the carcass. The selected cuts of beef were vacuum packaged and transported to the Kansas State University (KSU) meat laboratory for fabrication. The fabricated steaks were assigned to one of the three groups: Warner-Bratzler Shear Force (WBSF), trained sensory analysis, and biochemical analysis (sarcomere length, proteolysis, intramuscular fat content, collagen crosslink and content), as well as two aging periods: 2 or 21 d. 

 All data were analyzed as a split-plot using PROC GLIMMIX of SAS (version 9.4, Cary, NC). The model included the whole-plot factor of meat cut, the sub-plot factors of aging time and the cut × aging time interaction. The PROC CORR procedure of SAS was used to determine Pearson’s correlation coefficients between all tenderness contributors measured in this study to the overall tenderness evaluated by the trained panelist. The correlation analysis was determined among the variables for each muscle and across all 8 muscles.


Biochemical composition of the 8 beef cuts are displayed in table 1. As expected, all muscles studied increased in TNT degradation from 2 to 21 days of postmortem storage (P < 0.01). At two days, postmortem, LT, RF, GM, and SS all displayed a greater amount of TNT degradation compared to PP (P < 0.05), while RA, ST, and TB did not differ from PP (P > 0.10). At 21 days postmortem, LT, PP, RF, ST, SS, and TB all had a more significant amount of TNT degradation compared to RA (P < 0.05), while GM was not different from RA (P > 0.10). PP, RF, ST, SS, and TB all displayed greater collagen content compared to LT and RA (P < 0.05), while GM was not different in collagen content compared to LT and RA (P > 0.10). PP and SS had greater PYD densities compared to LT, ST, TB, and GM (P < 0.05), while RA did not differ from PP and SS in PYD density (P > 0.10).  PP and RA displayed the longest sarcomeres, followed by ST, RF, TB, and SS, with LT and GM displaying the shortest sarcomeres among the eight muscles evaluated (P < 0.05). RA and LT displayed the first and second greatest lipid contents, followed by GM, RF, SS, and PP, with ST having the lowest lipid content among the eight muscles evaluated (P < 0.05). 

 As expected, PP had the greatest WBSF value, followed by SS, ST, GM, and RA, with RF, TB, and LT exhibiting the lowest WBSF values among all (P < 0.05). There was also an aging effect (P < 0.01; figure 9), in which all the muscles decreased in shear force from two days (5.20 kgf) to 21 days (4.45 kgf) of postmortem storage. At two days postmortem, PP had the highest ratings for connective tissue amount, followed by RA, SS, ST, and GM, with longissimus thoracis, RF, and TB rated with the least amount of connective tissue (P < 0.05). It was interesting to note that trained panels observed a decrease in connective tissue amount for most of the muscles (P < 0.05) investigated in this study except for PP and RF (P > 0.10). In general, LT and RA had the greatest myofibrillar tenderness ratings, followed by TB and RF, with GM, ST and SS categorized being slightly tougher. Again, PP was rated with the lowest myofibrillar tenderness among all the muscles evaluated (P < 0.05). There was also an aging effect detected for myofibrillar tenderness rated by trained panelists (P < 0.01; figure 10), which the muscles increased in myofibrillar tenderness ratings from 2 days (55.66) to 21 days (62.99) of postmortem storage. RA had the greatest rating for lipid flavor, followed by LT, PP, RF, SS, and TB, with GM and ST, ranked with the lowest ratings for lipid flavor intensity (P < 0.05). At two days of aging, LT, RF, and TB had greater overall tenderness rating in comparison to GM, ST, and SS, while RA was not different from any of the six muscles. Again, PP exhibited the lowest overall tenderness rated by trained panelists (P < 0.05). This same trend was also observed at 21 days postmortem with LT, displaying the highest overall tenderness rating. RA and TB displayed the second highest ratings, followed by GM, RF, ST, and SS, with PP again displayed the lowest ratings for overall tenderness at 21 days (P < 0.05). It is interesting to note that RF and PP only improved minimally (~3) in overall tenderness after 21 days of postmortem aging, while the rest of the muscles improved by 9 – 19 in overall tenderness rating on a 100 scale. 

 The overall tenderness for GM showed a negative correlation with DPD density (r = -0.48; P < 0.05) and a positive correlation with lipid content (r = 0.51; P < 0.05). The overall tenderness for PP showed a negative correlation with collagen content (r = -0.48; P < 0.05) and a positive correlation with PYD density (r = 0.52; P < 0.05). There was a positive correlation for overall tenderness and TNT degradation for RA, ST and TB (r = 0.45, 0.55, and 0.55, respectively; P < 0.05), and there was a tendency for the same relationship for LT (r = 0.43; P < 0.10). The overall tenderness for SS showed tendency for a negative correlation with lipid content (r = -0.38; P < 0.10). Finally, the overall tenderness value for the combination of all eight cuts used in this study (n = 160) showed positive correlations for TNT (r = 0.33; P < 0.01) and lipid content (r = 0.22; P < 0.01), and a negative correlation for collagen content (r = -0.23; P < 0.01), PYD density (r = -0.24; P < 0.01), and sarcomere length (r = -.41; P < 0.01).

industry Implications

Although there is not a single tenderness component that can be used to predict beef tenderness, one can potentially predict beef tenderness on a carcass base utilizing a computational model generated with tenderness factors evaluated in this study. However, a large number of tenderness/muscle biochemical data are needed to strengthen the robustness of this modeling approach. Therefore, it is crucial to continue the current effort of promoting a shared international meat science research database to ensure the best use of available resources.