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 (TME n) 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 TME n 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 TME n. First order polynomial model showed high coefficients of determination (r 2 = 0.859). Artificial Neural Network model also showed high prediction accuracy (r 2 = 0.992). Principal Component Analysis was successfully used in prediction of TME n .

Physicochemical Changes of the Gluten-Free Rice-Buckwheat Cookies during Storage – Artificial Neural Network Model

“…Sensitivity analysis was used to investigate the influence of input variables on the observed outputs, evaluated at specific centile points for each input variable [27]. This analysis is also necessary to check if the ANN could behave erroneously [28].…”

“…This analysis is also necessary to check if the ANN could behave erroneously [28]. The infinitesimal amount has been added to each input variable in 10 equally spaced individual points determined by the minimum and maximum of the training data to check the influence of input variables on the observed outputs [27].…”

“…The common 'goodness of fit' tests and residual analysis were performed [27] and the obtained results are shown in Table 2. The obtained r 2 values were high, and χ 2 , MBE, RMSE, and MPE were low, indicating that this model generally shows a good fit to experimental data in the experimental domain at which points were not included in the regression, as mentioned earlier by Madamba [30].…”