Effect of moisture content on thermodynamic characteristics of grape: Mathematical and artificial neural network modelling

Abstract

Artificial neural networks (ANNs) and four empirical mathematical models, namely Henderson, GAB, Halsey, and Oswin were used for the estimation of equilibrium moisture content (EMC) of the dried grape (black currant). The results showed that the EMC of the grape were more accurately predicted by ANN models than by the empirical models. The heat and entropy of sorption of the grape have separately been predicted by two mathematical models as a function of EMC with desirable coefficient of determination (R2 ≈ 0.99). At the EMC above 7% (d.b.), the heat and entropy of the grape sorption were smoothly decreased, while they were the highest at the moisture content of about 7% (d.b.). Better equations could be developed for the prediction of the heat of sorption and entropy based on the data from the ANN model.