Modeling of microwave pretreatment effect on the oil extraction from tomato seeds by artificial neural network method

Increasing consumers demand for natural and additive-free foods and high volumes of food industry wastes, are stimulating the use of these resources in other food industries. Tomato pomace is one of the food factory wastes is the resulting by-product of tomato paste and sauce factories. The aim of this study was to evaluate the effect extraction method and microwave pretreatment of tomato seeds on the physicochemical characteristics of their extracted oil. The seeds were treated with microwaves using various power levels (0, 200 and 500 W) and different process times (0, 1, 3 and 5 min) and their oil was extracted by Soxhlet and press methods. Fatty acids composition of oils was determined by gas chromatography. Some physicochemical characteristics of extracted seed oil including oil yield, viscosity, acid value, peroxide value, and color index (L, b, a values) were evaluated. Data was analyzed with factorial treatment structure in a Completely Randomized Design in three replications. The experimental data was modeled by artificial neural network with 3 inputs (extraction method, microwave power and pretreatment time) and 7 outputs (oil yield, acid value, peroxide value, viscosity, L value, b value and a value). The results of artificial neural network modeling showed that the network with a 3-8-7 structure and using the Hyperbolic tangent activation function can predict the oil yield, acid value, peroxide value, viscosity, L value, b value and a value of tomato seed oil with high correlation coefficient and low error. Based on the results of the sensitivity analysis, the extraction method compared to the power and time of microwave assisted pretreatment of seeds was determined as the main factor.