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

• Published in: Meat science Vol. 93; no. 3; pp. 771 - 775
• Main Authors: Jerez-Timaure, N., Huerta-Leidenz, N., Ortega, J., Rodas-González, A.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2013
• Subjects: Adipose Tissue - metabolism; Animals; Body Composition; Body Weight; Bone and Bones - physiology; Breeding; Carcass; Cattle; Consumer Behavior; Databases, Factual; Dietary Fats - metabolism; Male; Mathematical Concepts; Meat - analysis; Multivariate Analysis; Phenotype; Prediction equation; Principal Component Analysis - methods; Principal component regression; Quality; Regression Analysis; Sex Factors; Stress, Mechanical; Taste; Tenderness; Venezuela; Venezuela; Principal component regression; Prediction equation; Quality; Carcass; Tenderness
• ISSN: 0309-1740; 1873-4138
• DOI: 10.1016/j.meatsci.2012.11.026

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: WBSFsteers=3.566+0.003(CW)−0.033(FC)−0.015(FT)+0.0004(SM); WBSFheifers=4.824+0.002(CW)−0.229(FC)+0.096(FT)−0.064(SM); WBSFbulls=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. ► Multivariate analysis allows getting reliable predictors for beef tenderness. ► There is a high variation (88%) in WBSF due to its regression with carcass traits. ► Fat cover score and bone maturity contributed with the variation in WBSF. ► Prediction equations for WBSF would improve the current carcass grading system.