Improving neural network prediction of amino acid levels in feed ingredients

Artificial neural networks (ANN) were trained to predict the amino acid (AA ) profile of feed ingredients. The ANN more effectively identified the comp lex relationship between nutrients and feed ingredients than linear regress ion (LR). Three types of ANN (NeuroShell. 2 (R)): three-layer backpropagati on (BP3), Ward Backpropagation (TM) (WBP), a general regression neural netw ork (GRNN); and LR (SAS (R) Proc GLM) were used to predict the AA level in corn, soybean meal, meat and bone meal, fish meal, and wheat based on proxi mate analysis. Ln contrast to a Fast study, a variety of alternative ANN tr aining parameters were examined to improve ANN performance. Predictive perf ormance was judged on the basis of the maximum R-2 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 deve loped, 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-2 values (GRNN) were: Me t, 0.92; TSAA, 0.94; and Lys, 0.90. Current meat and bone mean maximum R-2 values (GRNN) were: Met, 0.97; TSAA, 0.97; and Lys, 0.97. The ANN computati on is a successful alternative to statistical regression analysis for predi cting AA levels in feed ingredients.