Multivariate modeling to estimate the composition of carcass tissues of Santa Inês sheep

Abstract

The purpose of this study was to establish a multivariate model using two complementary multivariate statistical techniques: Factor Analysis and Stepwise Multiple Regression, to predict tissue composition through carcass characteristics of Santa Inês sheep. The data was obtained from 82 Santa Inês sheep under confinement. The predictor variables were carcass characteristics related to weight, yield, morphometric measures and meat cuts. The use of latent variables from factor analysis in multiple regression models eliminates the problem of multicollinearity of the explanatory variables, improving the accuracy of interpretation of results by proposing a better fit of the mathematical model. However, the coefficient of determination (R²) values were moderate for muscle proportion and total fat, and low for bone proportion, indicating that more appropriate independent variables should be used to better predict the proportion of tissues in Santa Inês sheep.