Modeling of Physicochemical Properties of Eggplant Slices Coated with Basil Seed Gum during Frying Process

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 methods to reduce the oil uptake, moisture retention, and improving the appearance properties of fried foods. In this study, different concentrations of basil seed gum (0.0, 0.5, 1 and 1.5% w/w) were used to coat the eggplant slices during deep-frying at 150, 175 and 200°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 basil seed gum reduced the oil uptake of the final product. Coating pretreatment maintained the final product moisture and moisture content of the sample coated with 1.5% gum was higher than the other samples (64.05%). This process was modeled by genetic algorithm-artificial neural network method with 2 inputs that included basil seed gum concentration and frying temperature and 5 outputs that included oil percentage, moisture content, and three main color indexes (yellowness(b*), redness (a*), and lightness (L*)). The results of modeling showed that a network with 4 neurons in a hidden layer and using the hyperbolic tangent activation function can predict the physicochemical properties of fried eggplant slices. The coating containing 1.5% of basil seed gum retained moisture content and reduced oil absorption by the fried samples, and this coating is recommended as a suitable edible coating for coating of eggplant slices before the frying process. Sensitivity analysis results showed that the changes in the concentration of basil seed gum had the highest effect on the lightness index and then on the oil content of fried eggplant slices. The change of frying temperature also had the highest effect on the lightness index of fried samples.