Prediction of Effect of Natural Antioxidant Compounds on Hazelnut Oil Oxidation by Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network

YALÇIN H., Ozturk I., Karaman S., Kisi O., Sagdic O., Kayacier A.

JOURNAL OF FOOD SCIENCE, vol.76, no.4, 2011 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 76 Issue: 4
  • Publication Date: 2011
  • Doi Number: 10.1111/j.1750-3841.2011.02139.x
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: ANFIS, ANN, hazelnut oil oxidation, natural antioxidants, peroxide, PEEL EXTRACT, CORN-OIL, STABILITY, STABILIZATION, VISCOSITY, EFFICACY, LOGIC, OLIVE, ACIDS
  • Yıldız Technical University Affiliated: No


In this study, natural compounds including gallic acid, ellagic acid, quercetin, beta-carotene, and retinol were used as antioxidant agents in order to prevent and decrease oxidation in hazelnut oil. Quercetin showed the strongest antioxidative effect among the antioxidative agents, during storage. The accuracy of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models was studied to estimate the oil samples' peroxide value (PV), free fatty acid (FFA), and iodine values (IV). The root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) statistics were used to evaluate the models' accuracy. Comparison of the models showed that the ANFIS model performed better than the ANN and multiple linear regressions (MLR) models for estimating the PV, FFA, and IV. The values of R2 and RMSE were found to be 0.9966 and 2.51, 0.6269 and 88.55, 0.5120 and 101.8 for the ANFIS, ANN, and MLR models for PV in testing period, respectively. The MLR was found to be insufficient for estimating various properties of the oil samples.