Multiple linear regression to predict carcass tissue composition in hair lambs raised under commercial system
Abstract
The aim of the present study was to predict the carcass tissue composition of hair lambs reared on a commercial system, based on the characteristics of commercial cuts using multiple linear regression. In the study, thirty crossbred male lambs (Pelibuey × Dorper/Katahdin), with an average live weight of 51.12 ± 0.97 kg, were used. After slaughter of lambs, the carcasses were stored in refrigeration at 4 °C for 24 hours. Subsequently, they were weighed and split longitudinally. The left half of carcasses was divided into eight cuts (shank, neck, shoulder, rib, flank, loin, sirloin, and leg), which were individually weighed (kg) and dissected into muscle, fat, and bone. Also, the total weight of muscle, total fat content, and total bone content in the complete carcass was determined. In general, total weight of muscle, total fat content, and total bone contentshowed moderate to high positive correlations (0.32 ≤ r ≤ 0.87; P < 0.05, P < 0.001) with the characteristics of commercial cuts. The best predictors of total muscle content were shoulder muscle content, shank weight, leg muscle content, and rib muscle content (R² = 0.96; MSE = 3.94; AIC = -1.28). The total fat content can be adequately predicted using rib fat content, loin fat content, and shoulder fat content (R²=0.96; MSE=3.29; AIC=- 7.71). While total bone content can be predicted from leg bone content, sirloin bone content, shoulder bone content, and shank bone content (R2 = 0.91; MSE = 0.75; AIC = -16.42). All linear regression equations were found to be significant (P < .001). It is concluded that the carcass tissue composition of hair lambs is highly correlated with characteristics of commercial cuts. Consequently, the regression equations obtained in the study had high accuracy. Therefore, they can be used by technicians, producers, and researchers to obtain information on the carcass composition of hair lambs reared on commercial systems.
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