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Prediction of Effect of Natural Antioxidant Compounds on Hazelnut Oil Oxidation by Adaptive Neuro‐Fuzzy Inference System and Artificial Neural Network
Author(s) -
Yalcin Hasan,
Ozturk Ismet,
Karaman Safa,
Kisi Ozgur,
Sagdic Osman,
Kayacier Ahmed
Publication year - 2011
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1750-3841.2011.02139.x
Subject(s) - adaptive neuro fuzzy inference system , mean squared error , neuro fuzzy , gallic acid , artificial neural network , coefficient of determination , fuzzy logic , mathematics , quercetin , artificial intelligence , statistics , biological system , machine learning , computer science , fuzzy control system , chemistry , antioxidant , biology , biochemistry
In this study, natural compounds including gallic acid, ellagic acid, quercetin, β‐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 ( R 2 ) 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 R 2 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. Practical Application: Fuzzy logic and fuzzy inference systems are common and effective modeling techniques among the mathematical models. They have no expert dependency for the generation of rules and nonadaptive fuzzy set design. They can be used to solve problems without using accurate mathematical models. There are numerous studies about the modeling and identification of food characteristics using the fuzzy inference system (FIS) in the food engineering field. Many model food studies have been performed to determine the antioxidative effects of different plant extracts, phenolic compounds, and carotenoids in a model oil system. However, there is no report in the literature on antioxidative effects of gallic acid (phenolic acid), ellagic acid (polyphenolic acid), quercetin (flavonol), β‐carotene, and retinol in hazelnut oil. For this reason, in our study these natural compounds were used as antioxidant agents and their effectiveness on oxidation parameters were determined during a long storage period.