Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/17/1998
Publication Date: N/A
Citation: N/A Interpretive Summary: At present, beef carcass value is a function of USDA quality grade, a subjective estimate of meat palatability, and USDA yield grade, a subjective estimate of carcass composition. Although USDA yield grade is a relatively accurate predictor of carcass composition, producers continue to distrust use of yield grade in pricing formulas because of its subjectivity. Thus, it is widely believed that development of an accurate, objective method of estimating carcass composition would facilitate value-based marketing. This experiment involved the development and testing of a highly-accurate, rapid, automated system to predict beef carcass composition based on computerized analysis of a digital picture of a beef rib steak. This system, which is more accurate than previous image analysis systems, can be combined with technology for tenderness classification of beef to simultaneously characterize beef for tenderness and carcass composition. These tools should help facilitate the development of value-based marketing systems.
Technical Abstract: The present experiment was conducted to determine if image analysis of the 12th rib cross section used for tenderness classification could accurately predict carcass cutability, longissimus area, and subprimal cut weights. The right side of crossbred steer and heifer carcasses (n = 66) was fabricated and the yield of totally-trimmed retail product was determined. Following procedures we have described for tenderness classification, a 2.54-cm thick steak was removed from the 12th rib region of the left side of each carcass and image analysis was conducted. Image analysis accounted for more of the variation in retail product yield (RPYD; 89% vs 77%) and retail product weight (95% vs 90%) than did calculated yield grade. Also, image analysis accurately predicted longissimus area (R = .94). For most subprimals, the combination of image analysis-predicted RPYD and hot carcass weight (HCW) accounted for more of the variation in subprimal weight than did the combination of calculated yield grade and HCW. Whereas HCW, by itself, only accounted for 30 to 34% of the variation in weights of round cuts, the combination of image analysis-predicted RPYD and HCW accounted for 78 to 82% of the variation in weights of round cuts. Hot carcass weight, the combination of calculated yield grade and HCW, and the combination of image analysis-predicted RPYD and HCW accounted for 54, 83, and 91% of the variation in the weight of 80% lean trimmings. Thus, image analysis could be used by the beef industry in combination with tenderness classification to accurately characterize beef carcasses for cutability and tenderness. These tools should help facilitate the development of value-based marketing systems.