alexa Keys to Selecting a Prediction Model for Carcass Compos

Journal of Simulation and Computation
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Research Article

Keys to Selecting a Prediction Model for Carcass Composition from Computed Tomography Images

Font-i-Furnols M*, Carabus A and Gispert M

IRTA-Product Quality, Finca Camps i Armet, 17121 Monells, Girona, Catalonia, Spain

*Corresponding Author:
Font-i-Furnols M
IRTA-Product Quality, Finca Camps i Armet
17121 Monells, Girona, Catalonia, Spain
Tel: 34972630052
E-mail: [email protected]

Received date: February 15, 2016; Accepted date: February 28, 2016; Published date: March 6, 2016

Citation: Font-i-Furnols M, Carabus A, Gispert M (2016) Keys to Selecting a Prediction Model for Carcass Composition from Computed Tomography Images. J Tomogr Simul 1:104.

Copyright: © 2016 Font-i-Furnols M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Linear, nonlinear and volume measurements obtained from computed tomography (CT) images of live pigs are good predictors of carcass characteristics there are different ways to analyse the goodness of a prediction equation, including the decomposition of the predicted error and the biases and coefficient of determination. The present paper compares the goodness of fit of individual prediction equations within three different genotypes and the prediction obtained by a global equation for the different genotypes at the same time comparison is performed by means of the error decomposition, the standard deviation of the bias and the coefficient of model determination The results showed a good mean square prediction error and a high error due to disturbances (random effects) for most of the predictions; however, the prediction of lean obtained by the global equation and applied to the specific genotypes presented a low error due to disturbances and a high error due to central tendency different results are obtained when comparing individual and global equations for the estimation of lean without the distinction of the genotype predicted In general, the comparison shows that both equations are properly developed and useful; however, the utility is not the same for both of them.


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