Artificial Intelligence and Mathematical Modelling of the Drying Kinetics of Pre-treated Whole Apricots
Résumé
This study involved monitoring and modelling the drying kinetics of whole apricots pre-treated with solutions of sucrose, NaCl, and sodium bisulphite. The drying was performed in a microwave oven at different power levels (200, 400, and 800 W). Two artificial intelligence models were used for the prediction of drying time (DT) and moisture ratio (MR): artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). On the other hand, the MR prediction was also done with 21 semi-empirical models, one of which we created. The results showed that the drying time decreased with the increase in microwave oven power for the three treatments. The treatment with NaCl was the most suitable for our work. The correlation coefficients of drying time (0.9992) and moisture ratio (0.9997) of ANN were high compared to the ANFIS model, which were 0.9941 and 0.9995, respectively. Among twenty semi-empirical models that were simulated, three models were fitted to our study (Henderson and Papis modified, Henderson and Pabis, and hvo terms). By comparing the three models adapted to our work and the model that we proposed, as well as ANN for MR prediction, it was observed that the model that we created was the most appropriate for describing the drying kinetics of NaCl-treated apricot. This solution opens the prospect of using this potential model to simulate fruit and vegetable drying kinetics in the future.
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