Multiple regression and principal component analysis to study carcass and meat quality characteristics of beef cattle

Context Accurate prediction of carcass and meat quality traits is essential for decision-making in beef cattle production systems, but predictive performance varies among traits. Aims This study evaluated linear regression models for predicting carcass and meat quality traits in Nellore cattle. Methods Data from 698 Nellore bulls (22–24 months of age) finished in a feedlot were analyzed, including performance variables, carcass traits (hot carcass weight [HCW], dressing percentage, ribeye area, subcutaneous fat thickness and marbling score) and meat quality attributes (pH, color variables [L*, a*, b*], water-holding capacity, shear force and cooking weight loss). Model performance was assessed using the adjusted coefficient of determination (R2adj) to quantify goodness-of-fit, and predictive ability was evaluated using cross-validated R2 (R2pred). Principal component analysis was applied to investigate relationships among traits. Results HCW and meat color parameters (a* and b*) were accurately predicted by linear regression models (R2adj up to 0.99, 0.96 and 0.94, respectively; R2pred up to 0.99, 0.96 and 0.93), whereas ribeye area, subcutaneous fat thickness and shear force showed low predictive performance (R2adj and R2pred < 0.30). Principal component analysis indicated that the first four components explained >60% of total data variation, revealing positive associations of HCW and final bodyweight with meat redness and yellowness, whereas shear force and cooking losses were inversely associated with subcutaneous fat thickness. Conclusion Linear regression models effectively predicted HCW and meat color traits in Nellore bulls, but showed limited accuracy for traits related to muscularity, fat deposition and tenderness. Implications Linear regression models can support management and breeding strategies aimed at improving carcass yield and visual meat quality; however, improving predictions for tenderness and fat-related traits will require incorporation of additional biological and management variables.