Reducing NIR prediction errors with nonlinear methods and large populations of intact compound feedstuffs

According to the current demands of the authorities, the manufacturers and the consumers, controls and assessments of the feed compound manufacturing process have become a key concern. Among others, it must be assured that a given compound feed is well manufactured and labelled in terms of the ingredient composition. When near-infrared spectroscopy (NIRS) together with linear models were used for the prediction of the ingredient composition, the results were not always acceptable. Therefore, the performance of nonlinear methods has been investigated. Artificial neural networks and least squares support vector machines (LS-SVM) have been applied to a large (N = 20 320) and heterogeneous population of non-milled feed compounds for the NIR prediction of the inclusion percentage of wheat and sunflower meal, as representative of two different classes of ingredients. Compared to partial least squares regression, results showed considerable reductions of standard error of prediction values for both methods and ingredients: reductions of 45% with ANN and 49% with LS-SVM for wheat and reductions of 44% with ANN and 46% with LS-SVM for sunflower meal. These improvements together with the facility of NIRS technology to be implemented in the process make it ideal for meeting the requirements of the animal feed industry.