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Authors
Advisor(s)
Abstract(s)
The aim of this study was to develop and evaluate robust regression models for predicting the carcass composition of
lambs. One hundred and twenty lambs (34 females and 86 males) were slaughtered and their carcasses were cooled for
24 hours. The subcutaneous fat thickness (C12) was measured between the 12th and 13th rib, and the left side of
carcasses was dissected and the proportions of lean meat (LMP) was calculated. A multiple regression model was fitted
using robust regression (RR) methods, and the results were compared to ordinary least squares (OLS) estimates. For
RR methods, the Bisquare and Welsch weighting functions were used, and model fitting quality was evaluated by the
following statistics: the root mean square error (RMSE), the median absolute deviation (MAD), the mean absolute
error (MAE), and the coefficient of determination (R2). The parameters obtained by RR presented lower standard
error for C12 measurement (decreases by 12% when compared with OLS estimates). The RR methods or weighted least
squares methods represents a good alternative to OLS approach for modelling the LMP of lambs carcass. In this study,
the Bisquare weighting method presented the best results, however other weighting functions are available and should
be tested and compared in the near future.
Description
Keywords
Carcass Quality Ordinary least squares Robust regression
Citation
Xavier, Cristina; Cadavez, Vasco (2012). Robust regression models for predicting the lean meat proportion of lambs carcasses. Scientific Papers. Series D. Animal Science. ISSN 2285-5750. 55, p. 296-298