Improving neural network prediction of amino acid levels in feed ingredients

Abstract

Artificial neural networks (ANN) were trained to predict the amino acid (AA) profile of feed ingredients. The ANN more effectively identified the complex relationship between nutrients and feed ingredients than linear regression (LR). Three types of ANN (NeuroShell 2): three-layer backpropagation (BP3), Ward Backpropagation (WBP), a general regression neural network (GRNN); and LR (SAS Proc GLM) were used to predict the AA level in corn, soybean meal, meat and bone meal, fish meal, and wheat based on proximate analysis. In contrast to a past study, a variety of alternative ANN training parameters were examined to improve ANN performance. Predictive performance was judged on the basis of the maximum R2 value resulting from all defaults tested. Advanced selection of ANN training parameters led to further improvement in performance, especially within the GRNN architecture. In 34 of 35 ANN developed, the maximum R2 value for each individual AA in each feed ingredient was higher for GRNN than for LR, BP3, or WBP prediction methods. For example, the highest R2 value for Met in corn was 0.32 for LR, 0.40 for 3LBP, 0.51 for WBP, and 0.95 for GRNN analysis. Predictive performance was also improved overall as compared to results of a previous study. For example, corn maximum R2 values (GRNN) for Met, TSAA, and Trp were: 0.78, 0.81 and 0.44, previously, and 0.95, 0.96 and 0.88, in the current study. Current soybean meal maximum R values (GRNN) were: Met, 0.92; TSAA, 0.94; and Lys, 0.90. Current meat and bone mean maximum R2 values (GRNN) were: Met, 0.97; TSAA, 0.97; and Lys, 0.97. The ANN computation is a successful alternative to statistical regression analysis for predicting AA levels in feed ingredients.