Freeze-drying behaviour prediction of button mushrooms using artificial neural network and comparison with semi-empirical models

The application of artificial neural networks (ANN) in the freeze-drying of button mushrooms has been investigated. Networks with a single hidden layer, different training algorithms and complexity in terms of the number of neurons were evaluated for identifying the best ANN infrastructure. Moisture content, moisture ratio and drying rate were taken as output drying parameters for which ANN models provided an overall correlation coefficient (R) of 0.994, 0.991 and 0.992, respectively. The predictive efficiency of ANN was compared to semi-empirical models. Coefficients for semi-empirical models of moisture ratio were determined. Logarithm model gave the best fit (R2 = 0.985) for moisture ratio prediction but with larger mean square error and lower correlation than ANN model. The study highlights that ANN models with low complexity can be developed to precisely predict drying behaviour of biological materials while providing comparable and even superior results to that obtained from available semi-empirical drying models.