Ge. Arteaga et S. Nakai, PREDICTING PROTEIN FUNCTIONALITY WITH ARTIFICIAL NEURAL NETWORKS - FOAMING AND EMULSIFYING PROPERTIES, Journal of food science, 58(5), 1993, pp. 1152-1156
Using physicochemical properties of 11 food-related proteins, artifici
al neural networks (ANN) were developed for predicting foam capacity a
nd stability and the emulsion activity index. The prediction accuracy
of ANN was compared to that of principal component regression (PCR) mo
dels. ANN had better prediction ability than PCR, especially after cro
ss-validation. For foam stability, PCR did not generate a significant
model. ANN and PCR models indicated that fluorescence probe hydrophobi
city (measured using cis-parinaric acid) and other properties, such as
viscosity, surface tension and net charge were important in predictin
g foam capacity and stability.