Determination of freeze-drying behaviors of apples by artificial neural network
Freeze drying is the best drying technology regarding quality of the end product but it is an expensive method and the high costs of process limit its application to industrial scale. At the same time, the freeze-drying process is based on different parameters, such as drying time, pressure, sample thicknesses, chamber temperature, sample temperatures and relative humidity. So, the determination of drying behaviors, such as moisture content (MC), moisture ratio (MR) and drying rate (DR), of the freeze-drying process are too complex. In this paper, to help the freeze dryer designer and simplify this complex process, the use of artificial neural networks has been proposed. An artificial neural networks (ANN) model has been developed for determination the prediction of drying behaviors, such as MC, MR and DR, of apples in the freeze-drying process. The back-propagation learning algorithm with variant which is LevenbergMarquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R2), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R2, RMSE and MAPE have been determined for MC, MR and DR, as 0.999, 0.0078895, 0.2668459, and 0.999, 0.0001099, 0.2968427 and 0.999, 0.0000008, 0.2703797, respectively.
drying apple freeze drying ann modeling