caa a22 4500 606243712 CHVBK 20210128101326.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11694-015-9226-7 doi (NATIONALLICENCE)springer-10.1007/s11694-015-9226-7 Comparison of adaptive neuro-fuzzy inference system and artificial neural networks (MLP and RBF) for estimation of oxidation parameters of soybean oil added with curcumin [Elektronische Daten] [Elahe Asnaashari, Maryam Asnaashari, Ahmad Ehtiati, Reza Farahmandfar] A analysis of soybean oil oxidation in presence of different concentrations of active substrate of turmeric rhizome (curcumin) (0.012, 0.016 and 0.02%) at 25 and 55°C based on oxidation parameters including peroxide value (PV), acid value (AV) and iodine value (IV) at specific time interval, was performed. Adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) and radial basis function (RBF) functions of artificial neural network (ANN) with three inputs (temperature and concentration, time of sampling) and three outputs (PV, AV and IV) were used for the construction of models that could predict the oxidation parameters and were compared to multiple linear regression (MLR). It was shown that the ANFIS model (R2=0.98, 0.85 and 0.99 for PV, AV and IV, respectively) performed better compared to ANN (MLP and RBF) and MLR. Sensitivity analysis based on ANFIS model suggested the high sensitivity of oxidation parameters on temperature and concentrations of curcumin due to its high antioxidant activity to enhance soybean oil shelf life. Springer Science+Business Media New York, 2015 Adaptive neuro-fuzzy inference system nationallicence Artificial neural network nationallicence Curcumin nationallicence Lipid oxidation nationallicence Sensitivity analysis nationallicence Soybean oil nationallicence Asnaashari Elahe Department of Mathematics, Faculty of Science, Mashhad Branch, Islamic Azad University, Mashhad, Iran aut Asnaashari Maryam Department of Food Science and Technology, Sari Agricultural Sciences and Natural Resources University (SANRU), P.O. Box 578, Sari, Iran aut Ehtiati Ahmad Food Science and Technology Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran aut Farahmandfar Reza Department of Food Science and Technology, Sari Agricultural Sciences and Natural Resources University (SANRU), P.O. Box 578, Sari, Iran aut Journal of Food Measurement and Characterization Springer US; http://www.springer-ny.com 9/2(2015-06-01), 215-224 2193-4126 9:2<215 2015 9 11694 https://doi.org/10.1007/s11694-015-9226-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11694-015-9226-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Asnaashari Elahe Department of Mathematics, Faculty of Science, Mashhad Branch, Islamic Azad University, Mashhad, Iran aut NATIONALLICENCE 700 1- Asnaashari Maryam Department of Food Science and Technology, Sari Agricultural Sciences and Natural Resources University (SANRU), P.O. Box 578, Sari, Iran aut NATIONALLICENCE 700 1- Ehtiati Ahmad Food Science and Technology Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran aut NATIONALLICENCE 700 1- Farahmandfar Reza Department of Food Science and Technology, Sari Agricultural Sciences and Natural Resources University (SANRU), P.O. Box 578, Sari, Iran aut NATIONALLICENCE 773 0- Journal of Food Measurement and Characterization Springer US; http://www.springer-ny.com 9/2(2015-06-01), 215-224 2193-4126 9:2<215 2015 9 11694