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| Artificial neural network prediction of amino acid levels in feed ingredients. Researchers at The Pennsylvania State University also investigated
the use of artificial neural networks in predicting amino acid levels
in feed ingredients. Artificial neural networks are biological-inspired
tools that can serve as an alternative to regression analysis for complex
data. Based on crude protein and proximate analysis of ingredients,
two types of artificial neural networks and linear regression were evaluated
for predicting amino acid levels in common feed ingredients. The research
indicated that a general regression neural network (GRNN) program using
proximate analysis data provided better prediction of amino acid levels
in feed ingredients compared to linear regression. The research indicated
that neural networks appear to be a promising method for modeling the
relationship between proximate analysis of an ingredient and amino acid
composition. The neural networks can be incorporated into a computer
or spreadsheet program. The bottom line is that new computer programs
are being studied for their application to feed formulation. It may
be possible in the future to do a more precise job of formulating feeds
to meeting nutrient specifications, reducing nutrient excesses and minimizing
ration costs. |
