Multi-objective optimization of the apple drying and rehydration processes parameters

Abstract

The aim of the paper is to investigate the effect of drying and rehydration parameters on the quality of rehydrated apples and to optimize these parameters based on quality of the rehydrated products. Hybrid artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) method were successfully developed to model, simulate, and optimize the drying and rehydration parameters. This method was applied to the apple tissue, where the simultaneous maximization of the volume ratio (VR) and water absorption capacity (WAC) and the minimization of colour difference (CD) and dry matter loss (DML) were considered. The range of drying and rehydration parameters was: 50-70°C (drying temperature, T_d), 0.01-6 m×s^-1(drying air velocity, V), and 20-95°C (rehydration temperature, T_r). Additionally, the mathematical formulas for determing the quality parameters of rehydrated apple were developed. The best solution has been found only for: CD, VR, WAC (50.1°C for T_d, 0.03 m×s^-1 for V, and 67.5°C for T_r). The values of CD, VR, and WAC were predicted as 1.20, 53.2% and 0.57 respectively. It has been observed that DML was always conflicting with other quality parameters.