optimization of orange osmotic dehydration process using response surface method and estimation of dehydration parameters by artificial neural network

in this study, response surface methodology (rsm) was used to optimize osmo-dehydration of orange slice. effect of osmotic solution temperature in the range of 30 to 60 °c, immersion time from 0 to 300 min and sucrose concentration from 35 to 65 brix degree on water loss, solid gain, moisture content, water loss to solid gain ratio and brix change were investigated by central composite design (ccd). applying response surface and contour plots optimum for osmotic dehydration were found to be at temperature of 30 °c, immersion time of 229.2 minute and sucrose concentration of 65%. at this optimum point, water loss, solid gain, wl/sg ratio, moisture content (dry base) and brix difference were found to be 30.316 (g/100 g initial sample), 13.51 (g/100 g initial sample), 2.45, 2.77 % and 15.79, respectively. the result of artificial neural network indicated that the perceptron neural network with one hidden layer is able to anticipate the dehydration characteristics. this network predicted solid gain and moisture content with 5 neuron per hidden layers with r2 values of 0.937 and 0.959, respectively and brix difference and water loss with 30 neuron per hidden layer with r2 values of 0.961 and 0.942, respectively.