Intelligent Modeling and Analysis of Moisture Sorption Isotherms in Milk and Pearl Millet–Based Weaning Food “Fortified Nutrimix”

Abstract

Soft computing–based intelligent models have been proposed to predict moisture sorption isotherms in milk and pearl millet–based weaning food, “fortified Nutrimix,” at four temperatures, 15, 25, 35, and 45°C over the water activity range 0.11–0.97. Connectionist and adaptive neuro-fuzzy inference system (ANFIS) models were investigated. A back-propagation algorithm with Bayesian regularization/Levenberg-Marquardt optimization mechanisms was employed to develop connectionist models. The ANFIS model was based on the Sugeno-type fuzzy inference system. In addition, several empirical models were explored for fitting the sorption data. The soft computing models, in particular, ANFIS, outperformed the conventional sorption models for predicting isotherms in Nutrimix.

Keywords:

• Adaptive neuro-fuzzy inference system
• Connectionist models
• Fortified Nutrimix
• Moisture sorption isotherms
• Soft computing

Notes

Mean and SD values are based on three replicates.