This research aimed to develop a simple mathematical model able to predict the subcutaneous fat thickness of pig thighs starting from the carcass traits as weight, backfat and l.dorsi thickness, and weight of the trimmed thighs. A stepping model-building technique using the General Regression Model method, involving two steps, was implemented. Firstly, the best fit equation was developed which was able to describe relationships among the investigated variables. Thereafter, a validation step, to evaluate the predictive ability of the model, was performed against a set of independent data from that used in building step. Results proved that both the quadratic and interactive terms of carcass traits could be successfully used to predict the subcutaneous fat thickness of pig thighs. About 82% of the predicted data were in agreement with the experimental measurements.
Sviluppo di una equazione di stima dello spessore del grasso di copertura della coscia suina / LO FIEGO, Domenico Pietro; Falcone, Pm; Ielo, M. C.. - ELETTRONICO. - 61:(2007), pp. 457-458. (Intervento presentato al convegno SOCIETA' ITALIANA DELLE SCIENZE VETERINARIE tenutosi a Salsomaggiore Terme (PR) nel 26-29 SETTEMBRE 2007).
Sviluppo di una equazione di stima dello spessore del grasso di copertura della coscia suina
LO FIEGO, Domenico Pietro;
2007
Abstract
This research aimed to develop a simple mathematical model able to predict the subcutaneous fat thickness of pig thighs starting from the carcass traits as weight, backfat and l.dorsi thickness, and weight of the trimmed thighs. A stepping model-building technique using the General Regression Model method, involving two steps, was implemented. Firstly, the best fit equation was developed which was able to describe relationships among the investigated variables. Thereafter, a validation step, to evaluate the predictive ability of the model, was performed against a set of independent data from that used in building step. Results proved that both the quadratic and interactive terms of carcass traits could be successfully used to predict the subcutaneous fat thickness of pig thighs. About 82% of the predicted data were in agreement with the experimental measurements.Pubblicazioni consigliate
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