An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Energy efficiency and moisture diffusivity of apple slices during convective drying

Abstract The present study aimed at investigating the influences of drying air temperature and flow rate on energy parameters and dehydration behaviour of apple slices. For this purpose, apple slices were dried in a convective dryer at air temperatures of 50, 60 and 70 °C, and air velocities of 1, 1.5 and 2 m s–1. Dehydration rate increased as the air temperature and flow rate increased from 50 to 70 °C and 1 to 2 m s–1, respectively. The effective moisture diffusivity was determined to be in the range of 6.75×10–10-1.28×10–9 m2 s–1. Results of data analysis showed that the maximum energy consumption (23.94 kW h) belonged to 50 °C and 2 m s–1 and the minimum (13.89 kW h) belonged to 70 °C and 1 m s–1 treatment. Energy efficiency values were in the range of 2.87-9.11%. Moreover, the results indicated that any increment in the air temperature increases thermal and drying efficiencies while any increment in the air flow rate decreases both of them.