Committee neural network and weighted multiple regression to predict the energetic values of poultry feedstuffs

Flávia Cristina Martins Queiroz Mariano,Paulo Borges Rodrigues,Renata Ribeiro Alvarenga,Renato Ribeiro De Lima

doi:10.1590/s1678-3921.pab2020.v55.001199

Flávia Cristina Martins Queiroz Mariano, Paulo Borges Rodrigues + Show 2 more

Open Access

https://doi.org/10.1590/s1678-3921.pab2020.v55.001199

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Abstract

Abstract: The objective of this work was to compare the committee neural network (CNN) and weighted multiple linear regression (WMLR) models, in order to estimate the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feedstuffs. The prediction equation was adjusted by using a WMLR model and the meta-analysis principle. The models were compared by considering the correct prediction percentages, based on the classic prediction intervals and on the highest-probability density intervals, and by using a comparison test for proportions. The accuracy of the models was evaluated based on the values of the mean squared error, coefficient of determination, mean absolute deviation, mean absolute percentage error, and bias. Data from metabolic trials were used to compare the selected models. The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict AMEn values for energetic concentrate feedstuffs used by the poultry feed industry.

Highlights

The diversity of feedstuffs and their byproducts used in the formulation of poultry feeds lead to the need of a precise knowledge of the chemical composition and the metabolizable energy values of feedstuffs to allow of an adequate supply of energy to the animals
The committee neural network is the model that showed the highest accuracy of prediction, being recommended as the most accurate model to predict apparent metabolizable energy (AMEn) values for energetic concentrate feedstuffs used by the poultry feed industry
The test data used in the comparison of the models refer to 48 samples of energetic concentrate and protein concentrate feedstuffs, which were determined in metabolic bioassays conducted in the Poultry Section of the Departamento de Zootecnia, of the Universidade Federal de Lavras, in Lavras, Minas Gerais state, Brazil

Summary

Introduction

The diversity of feedstuffs and their byproducts used in the formulation of poultry feeds lead to the need of a precise knowledge of the chemical composition and the metabolizable energy values of feedstuffs to allow of an adequate supply of energy to the animals. One of the most direct ways to determine the metabolizable energy is to use prediction equations (Nascimento et al, 2011). Classical statistical methods have been applied to obtain prediction equations of the nitrogen-corrected apparent metabolizable energy (AMEn) of poultry feeds. In these equations, the energy values of the feedstuffs are established according to their chemical compositions. In the methods used to obtain these prediction equations two ways can be considered to develop a multiple linear regression, as follows: using a classical multiple regression analysis without any restriction or weighting (Rodrigues et al, 2002); or using the principle of meta-analysis, in which homogeneous groups are established as weighting factors (Nascimento et al, 2009; Mariano et al, 2012)

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