alexa Keys to Selecting a Prediction Model for Carcass Compos

Journal of Simulation and Computation
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

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.

 

Abstract

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.

Keywords

Share This Page

Additional Info

Loading
Loading Please wait..
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords