This study aims at investigating the thin-layer drying behavior of turnip slices in a multistage semi-industrial continuous band dryer. Turnip slices with the thickness of 4 mm were used for the drying experiments. The experiments were conducted at three air temperatures of 45, 60, and 75C, three air velocities of 1, 1.5, and 2 m/ s, and three belt linear speeds of 2.5, 6.5, and 10.5 mm/s with three replications for each treatment. To estimate the drying kinetic of turnip slices, six mathematical models were used to fit the experimental data of thin layer drying. Consequently, the Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the turnip slices. The effective moisture diffusivity varied from 8.37 3 10210 to 4.82 3 1029 m2/s. The energy of activation varied from 12.80 to 26.31 kJ/mol using Arrhenius type equation. After well training of the ANN models, proved that the ANN model was relatively better than the empirical models. The best neural network for the prediction of moisture ratio (MR) and drying rate was feed forward back-propagation with 4-10-10-2 structure, training algorithm of Bayesian regulation and threshold functions of tansigpurelin- logsig. The best R2 value for predication of MR and drying rate were 0.9990 and 0.9619, respectively.
