A comparative study on the efficiency of two modeling approaches for predicting moisture content of apple slice during drying

The efficiency of two different modeling approaches for predicting moisture content of apple slices during drying were evaluated and compared. The experiments were performed at four air‐drying temperatures and at three levels of air velocity in the convective hot air dryer. Moisture content of apple during drying was predicted using theoretical model and Artificial Neural Networks (ANNs) models. The theoretical model was developed by solving heat and mass transfer equations simultaneously using numerical technique. A multilayer perceptron (MLP) neural network and radial basis function (RBF) network were used to develop neural network modeling. The agreement between the experimental results and the theoretical model predictions was quite good by considering the shrinkage dependent effective diffusivity in the model. Among different ANN structures, the best results were obtained for the MLP network with two hidden layers based on the statistical criteria (the coefficient of determination [R²] and mean squared error [MSE]). Although both two approaches could reasonably forecast the moisture content of apple slice during drying, the ANN model demonstrated better goodness of fit than theoretical model. PRACTICAL APPLICATION: Moisture content and water activity are important parameters in production of dried fruit, which have traditionally been measured by destructive and time‐consuming methods. Hence, quick tools are required to evaluate the moisture concentration and monitor changes during drying. Modeling can be considered as a helpful tool for prediction of moisture content as a function of time and space. The results of this study show that the two types of models can successfully predict changes in moisture content during drying. Applying these models at the commercial level can reduce variability in product quality and improve repeatability of the drying process.