Prediction of feed abrasive value by artificial neural networks and multiple linear regression

Abstract

In order to evaluate rapid testing methods based on the relationship between feed abrasive value (FAV) and physicochemical properties (particle size, bulk density, dry matter (DM), soluble dry matter, water-holding capacity (WHC), ash, crud protein, neutral detergent fiber (NDF), physically effective NDF and non-fibrous carbohydrates (NFC)), 100 empirical dataset were used. Relationships were investigated using multiple linear regression (MLR) and artificial neural networks (ANNs). The mean relative error was significantly (P < 0.01) lower for ANN than MLR model. Globally, the non-linear ANN model approach is shown to provide a better prediction of FAV than linear multiple regression.