Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module.

Four experiments were conducted in two commercial packing plants to evaluate the effectiveness of a commercial online video image analysis (VIA) system (the Computer Vision System equipped with a BeefCam module [CVS BeefCam]) to predict tenderness of beef steaks using online measurements obtained at chain speeds. Longissimus muscle (LM) samples from the rib (Exp. 1, 2, and 4) or strip loin (Exp. 3) were obtained from each carcass and Warner-Bratzler shear force (WBSF) was measured after 14 d of aging. The CVS BeefCam output variable for LM area, adjusted for carcass weight (cm2/kg), was correlated (P < 0.05) with WBSF values in all experiments. The CVS BeefCam lean color measurements, a* and b*, were effective (P < 0.05) in all experiments for segregating carcasses into groups that produced LM steaks differing in WBSF values. Fat color measurements by CVS BeefCam were usually ineffective for segregating carcasses into groups differing in WBSF values; however, in Exp. 4, fat b* identified a group of carcasses that produced tough LM steaks. Quality grade factors accounted for 3, 18, 21, and 0% of the variation in WBSF among steaks in Exp. 1 (n = 399), 2 (n = 195), 3 (n = 304), and 4 (n = 184), respectively, whereas CVS BeefCam output variables accounted for 17, 30, 19, and 6% of the variation in WBSF among steaks in Exp. 1, 2, 3, and 4, respectively. A multiple linear regression equation developed with data from Exp. 2 accurately classified carcasses in Exp. 1 and 4 and thereby may be useful for decreasing the likelihood that a consumer would encounter a tough (WBSF > 4.5 kg) LM steak in a group classified as "tender" by CVS BeefCam compared with an unsorted population. Online measurements of beef carcasses by use of CVS BeefCam were useful for predicting the tenderness of beef LM steaks, and sorting carcasses using these measurements could aid in producing groups of beef carcasses with more uniform LM steak tenderness.