Effectiveness of the SmartMV prototype BeefCam System to sort beef carcasses into expected palatability groups

Prediction equations for Warner-Bratzler shear force using principal component regression analysis in Brahman-influenced Venezuelan cattle

A database consisting of 331 beef animals (Brahman-crossbred) was used to determine the multivariate relationships between carcass and beef palatability traits of Venezuelan cattle and to develop prediction equations for Warner-Bratzler shear force (WBSF). The first three principal components (PC) explained 77.53% of the standardized variance. Equations were obtained for each sex class and the total variability observed in WBSF could be explained by its orthogonal regression with carcass weight (CW), fat cover (FC), fat thickness (FT), and skeletal maturity (SM). Prediction equations were: WBSF(steers)=3.566+0.003(CW)-0.033(FC)-0.015(FT)+0.0004(SM); WBSF(heifers)=4.824+0.002(CW)-0.229(FC)+0.096(FT)-0.064(SM); WBSF(bulls)=3.516+0.009(CW)+0.154(FC)-0.129(FT)-0.006(SM). A higher proportion of the variation was explained by the PC when variables of greater weight were selected to define each PC. The equation set presented herein could become an important tool to improve the Venezuelan carcass grading system.

International perspective: characterisation of United States Department of Agriculture and Meat Standards Australia systems for assessing beef quality

The intent, in this manuscript, is to characterise the United States Department of Agriculture (USDA) and Meat Standards Australia (MSA) systems for assessing beef quality and to describe the research evidence that supports the principles involved in grade application. USDA beef quality grading standards rely on carcass-trait-only assessments of approximate age of the animal at harvest and amount of intramuscular fat (as marbling) inside the muscles. USDA beef quality grading started 82 years ago. Then, as now, because no traceability system was in place, each animal’s history (exact age, feeding regimen, management practices, etc.) was incomplete; those who assigned quality grades used indicators of age (physiological maturity) and plane of nutrition (amount of marbling), and they do so still. Since 1926, research studies have identified a multitude of palatability-determining live-animal factors (e.g. genetics, use of hormonal growth promotants, high-energy diet finishing) and carcass-treatment factors (e.g. electrical stimulation, tenderstretch carcass suspension, postmortem aging) that cannot be incorporated into a carcass-trait-only quality assessment system. The USA beef industry has depended on development of more than 100 beef brands – some using palatability assurance critical control point plans, total quality management (TQM) philosophies, USDA certification and process verification programs, or combinations of live-animal factors, carcass-treatment factors and carcass-trait constraints – to further differentiate fresh beef products. The MSA grading system is a TQM grading approach that incorporates animal-specific traits (e.g. genetics, sex, age), control of certain pre-harvest and post-harvest processes in the beef chain, cut-specific quality differences and consumer preferences, into a beef pricing system. A unique aspect of the MSA grading system is that the grades are assigned to cuts or muscles, not carcasses; cuts or muscles from the same carcass are assigned individual (and in many cases, different) grades that reflect differences in expected eating quality performance among the various cuts of beef further adjusted to reflect the influence of cut or muscle aging and alternative cooking methods. The MSA grading system is still being modified and refined (using results of an extensive, ongoing consumer testing program), but it represents the best existing example of a TQM grading approach for improving beef quality and palatability. Research studies have shown that the accuracy of palatability-level prediction by use of the two systems – USDA quality grades for US customers and consumers and MSA grades for Australian customers and consumers – is sufficient to justify their continued use for beef quality assessment.

The accuracy and repeatability of untrained laboratory consumer panelists in detecting differences in beef longissimus tenderness123

The objective of this study was to determine the accuracy and repeatability of untrained laboratory consumer panelists in detecting differences in beef longissimus tenderness. At 14 d postmortem, slice shear force was measured on one steak from 192 strip loins and used to select 54 strip loins and assign 18 of the strip loins to each of three tenderness classes (tender = < 15 kg, intermediate = 15 to 27 kg, and tough = > 27 kg). Sixty-eight untrained, laboratory consumer panelists evaluated paired steaks from each tenderness class in each of two sessions (12 total observations per panelist). Mean slice shear forces for "tender," "intermediate," and "tough" were 11.1, 21.0, and 32.2 kg, respectively. Mean tenderness ratings of the untrained laboratory consumer panel were different (P < 0.05) among tenderness classes (mean of 16 panelists = 6.2, 4.9, and 3.3 for tender, intermediate, and tough, respectively), and these differences were similar regardless of how many untrained panelists were averaged to determine the panel mean (4, 8, 12, or 16). The correlations (P < 0.01) between slice shear force and the mean untrained consumer panel tenderness rating (mean of 4, r = -0.82; mean of 8, r = -0.89; mean of 12, r = -0.91; and mean of 16, r = -0.92;) were similar. Overall repeatability of the untrained consumer panel was 0.80. Repeatability of individual untrained consumer panelists for tenderness rating was highly variable: 31% were > 0.80, 36% were 0.60 to 0.79, and 33% were < 0.60. Thirty-two percent of the consumers were both accurate (correlation to slice shear force = -0.75 to -1.00, P < 0.01) and repeatable (repeatability > 0.75). There is wide variability in the ability of untrained laboratory consumer panelists to detect differences in beef tenderness. Nonetheless, untrained consumer panels can accurately and repeatedly detect differences in beef tenderness under controlled laboratory conditions. An untrained laboratory consumer panel may be able to provide as effective an evaluation of beef longissimus tenderness as a trained descriptive attribute panel.