Prediction of moisture content in pre-osmosed and ultrasounded dried banana using genetic algorithm and neural network

In this study, application of a versatile approach for estimation moisture content of dried banana using neural network and genetic algorithm has been presented. The banana samples were dehydrated using two non-thermal processes namely osmotic and ultrasound pretreatments, at different solution concentrations and dehydration times and were then subjected to air drying at 60 and 80 °C for 4, 5 and 6 h. The processing conditions were considered as inputs of neural network to predict final moisture content of banana. Network structure and learning parameters were optimized using genetic algorithm. It was found that the designed networks containing 7 and 10 neurons in first and second hidden layers, respectively, give the best fitting to experimental data. This configuration could predict moisture content of dried banana with correlation coefficient of 0.94. In addition, sensitivity analysis showed that the two most sensitive input variables towards such prediction were drying time and temperature.

Research highlights▶ Osmotic dehydration and ultrasound process were applied as the non-thermal pretreatments before drying of banana for saving energy and ameliorating drying rate and the moisture content of dried samples were predicted using an intelligent model (i.e. genetic algorithm-artificial neural network). ▶ The network containing 7 and 10 neurons in first and second hidden layers, respectively, could predict moisture content with correlation coefficient of 0.94. ▶ The results of sensitivity analysis indicated that drying time and temperature were the most important parameters to the changes of moisture content of dried banana.