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

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.

Keywords

• true metabolisable energy
• broiler diets
• FOP
• ANN