<abstract xmlns="http://www.w3.org/1999/xhtml"><p> One of the approaches to improving performance testing of pigs is to look for mathematical solutions to increase the accuracy of calculations. This is mainly done through improvement of linear regression equations based on current data on performance tested pigs in Poland. The advances in computer technology and the improvements in mathematical analysis have made it possible to use artificial neural networks (ANNs) for prediction of carcass meat percentage in young pigs. The aim of the study was to compare the potential for live estimation of carcass meat percentage in pigs using two computational methods: linear regression equations and ANNs. The experiment used 654 gilts of six breeds, which were subjected to performance testing and slaughter analysis at the Pig Performance Testing Station (SKURTCh). The collected data were used to train ANNs to estimate carcass meat percentage in young pigs. Training was performed using the Levenberg- Marquardt algorithm. Next, meatiness estimated by ANNs was compared with the results obtained using linear modelling. It is concluded that based on the fattening and slaughter performance test results of live pigs, artificial neural networks (SSN23) are significantly more accurate in estimating carcass meat percentage in young pigs compared to the three-variable linear regression model 1. The difference in meatiness estimation between SSN23 and the four-variable linear regression model 2 was statistically non-significant in most of the breeds except Duroc and Pietrain, where the meatiness of young animals was estimated more accurately by the linear regression model.</p></abstract>

One of the approaches to improving performance testing of pigs is to look for mathematical solutions to increase the accuracy of calculations. This is mainly done through improvement of linear regression equations based on current data on performance tested pigs in Poland. The advances in computer technology and the improvements in mathematical analysis have made it possible to use artificial neural networks (ANNs) for prediction of carcass meat percentage in young pigs. The aim of the study was to compare the potential for live estimation of carcass meat percentage in pigs using two computational methods: linear regression equations and ANNs. The experiment used 654 gilts of six breeds, which were subjected to performance testing and slaughter analysis at the Pig Performance Testing Station (SKURTCh). The collected data were used to train ANNs to estimate carcass meat percentage in young pigs. Training was performed using the Levenberg- Marquardt algorithm. Next, meatiness estimated by ANNs was compared with the results obtained using linear modelling. It is concluded that based on the fattening and slaughter performance test results of live pigs, artificial neural networks (SSN23) are significantly more accurate in estimating carcass meat percentage in young pigs compared to the three-variable linear regression model 1. The difference in meatiness estimation between SSN23 and the four-variable linear regression model 2 was statistically non-significant in most of the breeds except Duroc and Pietrain, where the meatiness of young animals was estimated more accurately by the linear regression model.

Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks

Department of Genetics and Animal Breeding, National Research Institute of Animal Production, 32-083 Balice n. Kraków, Poland

Institute of Genetics and Animal Breeding, Polish Academy of Sciences, Postępu 36A, Jastrzębiec, 05-552 Magdalenka, Poland

Department of Informatics, National Research Institute of Animal Production, 32-083 Balice n. Kraków, Poland

Annals of Animal Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

One of the approaches to improving performance testing of pigs is to look for mathematical solutions to increase the accuracy of calculations. This is mainly...

Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks

Article Category: Quality and safety of animal origin products

Online veröffentlicht: 23. Jan. 2016

Seitenbereich: 275 - 286

Eingereicht: 23. Juli 2015

Akzeptiert: 31. Aug. 2015

DOI: https://doi.org/10.1515/aoas-2015-0057

Schlüsselwörter
pigs, performance testing of animals, carcass meat percentage, regression equations, artificial neural networks

© by Magdalena Szyndler-Nędza

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Prediction of Carcass Meat Percentage in Young Pigs Using Linear Regression Models and Artificial Neural Networks

Article Category: Quality and safety of animal origin products

Online veröffentlicht: 23. Jan. 2016

Seitenbereich: 275 - 286

Eingereicht: 23. Juli 2015

Akzeptiert: 31. Aug. 2015

DOI: https://doi.org/10.1515/aoas-2015-0057

Schlüsselwörterpigs, performance testing of animals, carcass meat percentage, regression equations, artificial neural networks

© by Magdalena Szyndler-Nędza

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
pigs, performance testing of animals, carcass meat percentage, regression equations, artificial neural networks