Modelling moisture sorption characteristics in dried acid casein using connectionist paradigm vis-à-vis classical methods

Abstract

Six connectionist models are proposed to predict sorption (adsorption and desorption) characteristics at three temperatures, i.e., 25°, 35° and 45 °C over a water activity range of 0.11–0.97 in dried acid casein prepared from buffalo skim milk. Also, several conventional empirical sorption models were used for fitting the sorption data. The dataset comprised of 210 data points. The Error Back Propagation (EBP) learning algorithm with Bayesian Regularisation/Levenberg-Marquardt optimisation techniques as well as various combinations of connectionist network parameters was employed. The connectionist models predicted the adsorption characteristics with an accuracy ranging between 1.32 and 2.60 Root Mean Square percent error (RMS%) as compared to the best (among six conventional empirical models used) GAB model, which attained RMS% between 1.92 and 5.77. While for desorption characteristics, the connectionist models attained RMS% between 1.56 and 4.08 vis-a-vis the best (among six conventional empirical models used) GAB model with RMS% ranging between 1.4 and 5.01. Hence, the results revealed that the connectionist models outperformed the conventional empirical models and, generally, best described the experimental adsorption and desorption data for dried acid casein prepared from buffalo skim milk.