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For improvement and better control of the quality of the final product under various conditions, it is useful to use mathematical simulation and modeling of drying curves. These approaches are usually applied for studying the factors present in the process, optimization of conditions and working factors and predicting the drying kinetics of product. Two intelligent tools including, artificial neural network (ANN) and genetic algorithm (GA), were used in the current paper for predicting the tomato drying kinetics. For this purpose, firstly, four mathematical models were taken from the literature, and they were matched with the empirical data. Then, the best fitting model of the tomato drying curve was chosen. According to the results, the model proposed by Aghbashlo et al (Agh-m) showed great performance for predicting the moisture ratio of the dried tomato slices. Moreover, the genetic algorithm was utilized for optimization of the best empirical model. Ultimately, the results were compared with the results observed in ANN and GA models. The result indicated that the GA model offers higher accuracy for predicting the moisture ratio of dried tomato with the correlation coefficient (R2) of 0.9987.

Predicting the moisture ratio of dried tomato slices using artificial neural network and genetic algorithm modeling