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Authors
Advisor(s)
Abstract(s)
The aim of this study was to develop and evaluate models for predicting the carcass composition of lambs.
Fortymale lambswere slaughtered and their carcasseswere cooled for 24 hours. The subcutaneous fat thickness
was measured between the 12th and 13th rib and breast bone tissue thickness was taken in the middle of the
second sternebrae. Left side of carcasses was dissected and the proportions of lean meat (LMP), subcutaneous
fat (SFP), intermuscular fat (IFP), kidney and knob channel fat (KCFP), and bone plus remainder (BP) were
obtained. Models were fitted using the seemingly unrelated regression (SUR) estimator which is novel in this
area, and compared to ordinary least squares (OLS) estimates. Models were validated using the PRESS statistic.
Our results showed that SUR estimator performed better in predicting LMP and IFP than the OLS estimator.
Although objective carcass classification systems could be improved by using the SUR estimator, it has never
been used before for predicting carcass composition.
Description
Keywords
Carcass Quality Ordinary least squares Seemingly unrelated regression
Citation
Cadavez, Vasco A. P.; Henningsen, Arne (2012). The use of seemingly unrelated regression (SUR) to predict the carcass composition of lambs. Meat Science. ISSN 0309-1740. 92:4, p. 548-553
Publisher
Elsevier