Predicting the effects of coating with different concentrations of wild sage seed gum on the characteristics of fried zucchini slices at various temperature by genetic algorithm-artificial neural network method

Fried food products are very popular due to their unique characteristics such as color, smell, taste and desirable texture. Controlling frying conditions and using edible hydrocolloid coatings (gums) is one of the best ways in reduction of oil uptake, moisture retention and improving the appearance properties of fried foods. In this study, different concentrations of wild sage seed gum (0, 0.5, 1 and 1.5%) were used to coating of zucchini slices during deep frying at 155, 170 and 185°C and the relationship between process parameters and the quality of final product were modeled by genetic algorithm-artificial neural network method. The results of this study showed that coating with wild sage seed gum reduced the oil uptake of the final product and in terms of appearance characteristics, the coated samples were lighter. Coating pretreatment maintained the final product moisture and the size of the samples coated with 1.5% gum was larger than the other samples (lower surface changes percent). This process was modeled by genetic algorithm-artificial neural network method with 2 inputs include wild sage seed gum concentration and frying temperature and 7 outputs  include oil percentage, moisture content, yellowness index (b*), redness index (a*), lightness index (L*), color changes intensity (ΔE) and surface changes. The results of modeling showed that a network with 5 neurons in a hidden layer and using the sigmoid activation function can predict the physicochemical properties of fried zucchini slices. Sensitivity analysis results showed that the changes in the concentration of wild sage seed gum had the highest effect on the yellowness index and then on the surface color changes intensity index of fried zucchini slices. Also, the change of frying temperature has the highest effects on the color changes intensity and lightness indexes of fried samples.