PREDICTING PROTEIN FUNCTIONALITY WITH ARTIFICIAL NEURAL NETWORKS - FOAMING AND EMULSIFYING PROPERTIES

Using physicochemical properties of 11 food-related proteins, artificial neural networks (ANN) were developed for predicting foam capacity and stability and the emulsion activity index. The prediction accuracy of ANN was compared to that of principal component regression (PCR) models. ANN had better prediction ability than PCR, especially after cross-validation. For foam stability, PCR did not generate a significant model. ANN and PCR models indicated that fluorescence probe hydrophobicity (measured using cis-parinaric acid) and other properties, such as viscosity, surface tension and net charge were important in predicting foam capacity and stability.