Statistical evaluation of thin-layer drying of banana.
Drying of food materials is a complex process of simultaneous heat and mass transfer within dried material and from its surface to the surroundings, caused by a number of transport mechanisms. There are several different methods of describing the complex simultaneous heat and moisture transport processes within drying material. Thin-layer drying models are important tools in mathematical modeling of drying processes. In scientific literature generally for evaluation of thin-layer models, that use approximation of experimental drying data, the values of the coefficient of determination (R2), correlation coefficient (r), reduced chi-square, root mean square error (RMSE), and mean bias error (MBE) are the most common criteria to select the best model. In this paper one approach for selection of a thin-layer drying model based on serial statistical criteria was proposed. Fresh banana were used in this study. The experimental data set of thin-layer drying kinetics of banana slices at four drying air temperatures (40, 50, 60 and 70 0C) and three drying air velocities (1, 2 and 3 ms-1) were obtained on the experimental setup, designed to imitate an industrial convective dryer. The experimental data of drying kinetic of banana slices were fitted to twenty four thin-layer mathematical models by means of indirect non-linear regression analysis. For each model and data set, the statistical performance index was calculated and models were ranked afterwards. After that, several statistical rejection criteria were checked. The performed statistical analysis shows that the Modified Henderson-Pabis model gives the best results for approximation of experimental drying data. The single statistical criterion cannot be used to select the thin-layer drying model. The model must always be chosen based on multiple statistical criteria.