Comparison of adaptive neuro-fuzzy inference system and artificial neural networks for estimation of oxidation parameters of sunflower oil added with some natural byproduct extracts.

BACKGROUND: Apple pomace, orange peel and potato peel, which have important antioxidative compounds in their structures, are byproducts obtained from fruit or vegetable processing. Use of vegetable extracts is popular and a common technique in the preservation of vegetable oils. Utilization of apple pomace, orange peel and potato peel extracts as natural antioxidant agents in refined sunflower oil during storage in order to reduce or retard oxidation was investigated. All byproduct extracts were added at 3000 ppm to sunflower oil and different nonlinear models were constructed for the estimation of oxidation parameters. RESULTS: Peroxide values of sunflower oil samples containing different natural extracts were found to be lower compared to control sample. Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) were used for the construction of models that could predict the oxidation parameters and were compared to multiple linear regression (MLR) for the determination of the best model with high accuracy. It was shown that the ANFIS model with high coefficient of determination (R(2) = 0.999) performed better compared to ANN (R(2) = 0.899) and MLR (R(2) = 0.636) for the prediction of oxidation parameters CONCLUSION: Incorporation of different natural byproduct extracts into sunflower oil provided an important retardation in oxidation during storage. Effective predictive models were constructed for the estimation of oxidation parameters using ANFIS and ANN modeling techniques. These models can be used to predict oxidative parameter values.