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Predicting Protein Functionality with Artificial Neural Networks: Foaming and Emulsifying Properties
Author(s) -
ARTEAGA G.E.,
NAKAI S.
Publication year - 1993
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.1993.tb06136.x
Subject(s) - artificial neural network , principal component analysis , emulsion , viscosity , chromatography , surface tension , chemistry , biological system , materials science , computer science , artificial intelligence , composite material , organic chemistry , biology , thermodynamics , physics
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 cispsrinaric acid) and other properties, such as viscosity, surface tension and net charge were important in predicting foam capacity and stability.