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Drying characteristics of button mushroom slices were determined using  microwave vacuum drier at various powers (130, 260, 380, 450 W) and absolute  pressures (200, 400, 600, 800 mbar). To select a suitable mathematical  model, 6 thin-layer drying models were fitted to the experimental data. The  fitting rates of models were assessed based on three parameters; highest R2,  lowest chi square () and root mean square error (RMSE). In addition, using  the experimental data, an ANN trained by standard back-propagation  algorithm, was developed in order to predict moisture ratio (MR) and drying  rate (DR) values based on the three input variables (drying time, absolute  pressure, microwave power). Different activation functions and several rules  were used to assess percentage error between the desired and the predicted  values. According to our findings, Midilli et al. model showed a reasonable  fitting with experimental data. While, the ANN model showed its high  capability to predict the MR and DR quite well with determination  coefficients (R2) of 0.9991, 0.9995 and 0.9996 for training, validation and  testing, respectively. Furthermore, their predictions Mean Square Error were  0.00086, 0.00042 and 0.00052, respectively

Comparison of mathematical models and artificial neural networks for prediction of drying kinetics of mushroom in microwave vacuum dryer