Mass Transfer Kinetics of Eggplant during Osmotic Dehydration by Neural Networks

Mathematical models and artificial neural networks were applied to best describe water loss and solids gain during osmotic dehydration of eggplant in salt concentrations of 5, 10 and 15%, sample to osmotic solutions ratios of 1:10, 1:15 and 1:20 and temperatures of 30, 45 and 60C, supplemented by oven drying at 70C. Water loss and solids gain were calculated after 15, 30, 60, 90, 150, 210 and 270min. Temperature and salt concentrations had a direct relationship with effective water and salt diffusivities, which were in the range of 1.931x10(-9) -3.762x10(-10)m(2)/s and 1.371x10(-9) -7.061x10(-10)m(2)/s at different temperatures and salt concentrations, respectively. Generally, concentration of osmotic solution had a reverse relationship with activation energies of water loss and solids gain reactions. Predicting percentages of water loss and solids gain by artificial neural networks was maximized in topologies of 4-25-2 and 4-16-2 with R-2 coefficients of 0.9825 and 0.9761, respectively. Practical ApplicationsOsmotic dehydration (OD) of fruits and vegetables is usually carried out simply by their immersion in various types of hypertonic solutions. Nevertheless, determining details of mass transfer kinetics in those products is very crucial to achieve the optimal performance of OD process in fruits and vegetables before applying other efficient treatments and formulating a special product in food industry. In this paper, we investigated the effects of variable parameters of OD process on mass transfer kinetics, optimal duration of the process and performance ratio of this process for eggplant samples; besides, feasibility of predicting mass transfer kinetics by mathematical and artificial neural networks was evaluated. The results of this study will be helpful for all researchers and producers who want to know more about the nuances of impacts of different OD variables on kinetics of mass transfer in fruits and vegetables.