Use of different statistical approaches in prediction of metabolizable energy of diets for broilers

Aca Jovanovic,Dragan Palic,Radmilo Colovic,Lato Pezo

doi:10.5937/ffr1501073p

Abstract

Energy value of diets has importance for feed producers and farmers. Methods for in vivo determination of metabolisable energy have high accuracy, but they are time and cost consuming. The aim of this study was to investigate the effect of enzymatic digestible organic matter and values of proximate chemical analysis on prediction of the nitrogen corrected true metabolisable energy (TMEn) of diets for broilers. The performance of Artificial Neural Network was compared with the performance of first order polynomial model, as well as with experimental data in order to develop rapid and accurate method for prediction of TMEn content. Analysis of variance and post-hoc Tukey's HSD test at 95% confidence limit have been calculated to show significant differences between different samples. Response Surface Method has been applied for evaluation of TMEn. First order polynomial model showed high coefficients of determination (r2 = 0.859). Artificial Neural Network model also showed high prediction accuracy (r2 = 0.992). Principal Component Analysis was successfully used in prediction of TMEn.

Highlights

Energy value of diets has importance for feed producers and farmers
The Principal Component Analysis (PCA) applied to the given data set has shown a differentiation between the samples according to used process parameters, and it was used as a tool in exploratory data analysis to characterize and differentiate neural network input parameters (Figure 1)
crude fibre (CFi) content, crude ash (CA), true metabolisable energy (TMEn) and enzymatic digestibility of organic matter (EDOM) had been more influential for the first factor coordinate calculation, while crude fat (CFa) content had been more influential for the second factor coordinate calculation (67.3%, respectively)

Summary

Introduction

Energy value of diets has importance for feed producers and farmers. Since methods for in vivo metabolizable energy (ME) determination require the use of live animals they can be considered as most accurate. There has been a considerable interest to find accurate methods for ME prediction, which will be rapid and inexpensive (Robbins and Firman, 2005; Zhang et al, 1994). Lato Pezo et al, Use of different statistical approaches in prediction of metabolizable energy of diets for broilers, Food and Feed Research, 42 (1), 73-81, 2015. Developed empirical models show a reasonable fit to experimental data and successfully predict ME (Perai et al, 2010). Nonlinear models are found to be more suitable for real process simulation. First order polynomial (FOP), using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models have gained momentum for modelling and control of processes (Khuri and Mukhopadhyay, 2010; Priddy and Keller, 2005)

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Conclusion