Predicting quality of the whole egg powder during storage: Based on Arrhenius and radial basis function model

Abstract

The quality of whole egg powder (WEP) deteriorates during long-term storage due to oxidation, which causes caking, discoloration and reduction of functional properties. The purpose of this study is to investigate the changes in lipid oxidation, protein oxidation, and functional characteristics of WEP during accelerated storage and to establish an Arrhenius model and radial basis function neural network (RBFNN) model to predict the oxidative changes in WEP. During storage, the peroxide value (PV) and thiobarbituric acid reactive substances (TBARS) of lipid oxidation significantly increased, and the Fourier transform infrared spectroscopy results showed an increasing trend in the characteristic absorption peaks of lipid hydroperoxides, free fatty acids, and cis fatty acids. The carbonyl groups, surface hydrophobicity, and fluorescence intensity significantly decreased, and the functional characteristics showed a decreasing trend in the solubility, foaming, and emulsifying properties of WEP. Most importantly, compared to the Arrhenius model, the RBFNN model could predict and assess the lipid and protein quality of WEP more accurately, with a small mean squared error (MSE) (0.00048) for storage at 20–60 °C.