Kinetic analysis and back propagation neural network model for shelf-life estimation of stabilized rice bran

Abstract

To establish a general model for predicting shelf-life of stabilized rice bran, different samples with varying residual enzyme activity, water activity (aw), and packaging materials were used, and hot air assisted-radio frequency (HA-RF) heating was applied as stabilization method. Free fatty acids (FFA) were recorded during storage at different temperatures and kinetic analysis was conducted to compare the effects of different conditions on rice bran shelf-life. FFA content increased rapidly during storage, and higher aw and lipase (LA) activity corresponded to a higher FFA generation rate. Only the increase in FFA of samples with low LA activity (≤30 mg/g) and aw (≤0.3) conformed to Zero-order model, while that of other samples conformed to Fractional Conversion model. For the inhibitory effect of packaging materials on the generation of FFA, BOPP/Kraft-paper/PE > PA/PE > PET/PE > LDPE. A back propagation neural network (BPNN) model was also constructed to predict rice bran shelf-life, which employing LA activity, aw, packaging materials, and storage temperature as input parameters. The BPNN model trained by Levenberg–Marquardt achieved the best prediction performance, with coefficient of determination (R2) of 0.996 and root mean square error (RMSE) of 2.077. Rice bran packaged by BOPP/Kraft-paper/PE with LA activity of 20 mg/g and aw of 0.2 had the longest shelf-life of 147 d at 25 °C. This model is of great help in predicting the shelf-life of stabilized rice bran and guiding the development of rice bran stabilization process.