Intelligent models based nonlinear modeling for infrared drying of mahaleb puree

Abstract

In this article, nonlinear regressor models namely polynomial regressor, artificial neural-network (ANN) and least-squares support vector machine (LS-SVM) were designed and applied to model the drying kinetics and change of the antioxidant properties of mahaleb puree during infrared drying process. Temperature and time were used as the model inputs and moisture ratio, antioxidant capacity and total anthocyanin content were the outputs of nonlinear regressors. The regressor models were compared in terms of the root mean-squared-error (RMSE) and minimum-descriptive-length (MDL) criteria. According to statistical selection criteria, LS-SVM was the best model to describe the infrared drying kinetics of mahaleb puree with the lowest RMSE and MDL values. ANN with Levenberg-Marquardth optimization gave the best results to predict antioxidant capacity and total anthocyanin content during infrared drying process of mahaleb puree. Practical applications: (a) Design and analysis of intelligent models for modeling of drying processes. (b) Design of automatic drying equipments by embedding intelligent models. (c) Prediction of moisture ratio, antioxidant capacity and total anthocyanin of drying mahaleb puree at any time and temperature. © 2018 Wiley Periodicals, Inc.