Literature DB >> 22063893

The accuracy of predicting carcass composition of three different pig genetic lines by dual-energy X-ray absorptiometry.

M Marcoux1, L Faucitano, C Pomar.   

Abstract

The accuracy of dual energy X-ray absorptiometry (DXA) for predicting carcass composition was evaluated by applying this technology to 95 half-carcasses of gilts from three genetic lines of widely varying composition. The DXA measurements of total weight, fat and lean masses and bone mineral content were used to predict the dissected lean, fat, bone and weight of the major primal cuts and the overall carcass. The study showed that it is not necessary to correct, or rather adjust, the prediction equations between genetic lines, given the small decrease in the prediction error (less than 1.43%). Except for the prediction for loin bone weight, the results showed that incorporating more than one of the predictive variables derived from DXA or from direct carcass measurements into the regression models was not justified in terms of prediction accuracy. The best predictions were those for half-carcass weight and primal cut weights (R(2)>0.95). The prediction of dissected lean ranked second in accuracy (R(2)>0.85), with the exception of the belly. The equations using DXA measurements provide good estimation (R(2)>0.70) of the weight of fat in the carcass and primal cuts. However, DXA showed moderate accuracy (R(2)<0.66) in predicting bone weight, although greater accuracy was attained for loin bone weight using a two-variable prediction model.

Entities:  

Year:  2005        PMID: 22063893     DOI: 10.1016/j.meatsci.2005.02.015

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  1 in total

1.  Investigating the potential for genetic improvement of nitrogen and phosphorus efficiency in a Swiss large white pig population using chemical analysis.

Authors:  Claudia Kasper; Isabel Ruiz-Ascacibar; Peter Stoll; Giuseppe Bee
Journal:  J Anim Breed Genet       Date:  2020-03-20       Impact factor: 2.380

  1 in total

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