title: Prediction of oxidation parameters of purified Kilka fish oil including gallic acid and methyl gallate by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network.
abstract: BACKGROUND
  As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid and methyl gallate may be introduced as natural antioxidants to improve oxidative stability of marine oil. Since conventional modelling could not predict the oxidative parameters precisely, artificial neural network (ANN) and neuro-fuzzy inference system (ANFIS) modelling with three inputs, including type of antioxidant (gallic acid and methyl gallate), temperature (35, 45 and 55 °C) and concentration (0, 200, 400, 800 and 1600 mg L(-1) ) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1 ) and slope of propagation stage of oxidation curve (k2 ) and peroxide value at the IP (PVIP ) were performed to predict the oxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR).
  
  
  RESULTS
  The results showed ANFIS was the best model with high coefficient of determination (R(2)  = 0.99, 0.99, 0.92 and 0.77 for IP, k1 , k2 and PVIP , respectively). So, the RMSE and MAE values for IP were 7.49 and 4.92 in ANFIS model. However, they were to be 15.95 and 10.88 and 34.14 and 3.60 for the best MLP structure and MLR, respectively. So, MLR showed the minimum accuracy among the constructed models.
  
  
  CONCLUSIONS
  Sensitivity analysis based on the ANFIS model suggested a high sensitivity of oxidation parameters, particularly the induction period on concentrations of gallic acid and methyl gallate due to their high antioxidant activity to retard oil oxidation and enhanced Kilka oil shelf life. © 2016 Society of Chemical Industry.
authors.affiliation: Department of Food Science and Technology, Sari Agricultural Sciences & Natural Resources University (SANRU), P.O. Box 578, Sari, Iran.
authors.forename: Maryam
authors.initials: M
authors.lastname: Asnaashari
authors.affiliation: Department of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad, P.O. Box 91775-1163, Mashhad, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Farhoosh
authors.affiliation: Department of Food Science and Technology, Sari Agricultural Sciences & Natural Resources University (SANRU), P.O. Box 578, Sari, Iran.
authors.forename: Reza
authors.initials: R
authors.lastname: Farahmandfar
mesh_terms: D000975:Antioxidants / Q000737:chemistry*; D019587:Dietary Supplements / Q000032:analysis*; D005395:Fish Oils / Q000737:chemistry* / Q000302:isolation & purification; D005503:Food Additives / Q000737:chemistry*; D005506:Food Contamination / Q000517:prevention & control; D061353:Food Storage; D017143:Fuzzy Logic; D005707:Gallic Acid / Q000031:analogs & derivatives / Q000737:chemistry*; D006358:Hot Temperature / Q000009:adverse effects; D007492:Iran; D007700:Kinetics; D016014:Linear Models; D008054:Lipid Peroxides / Q000032:analysis / Q000737:chemistry; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010084:Oxidation-Reduction; D015203:Reproducibility of Results
chemical_list: D000975:Antioxidants; D005395:Fish Oils; D005503:Food Additives; D008054:Lipid Peroxides; C052082:methyl gallate; D005707:Gallic Acid
keywords: ANFIS; Artificial neural network; Gallic acid; Kilka fish oil; Lipid oxidation; MLR; Methyl gallate; Sensitivity analysis