Premium
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
Author(s) -
Asnaashari Maryam,
Farhoosh Reza,
Farahmandfar Reza
Publication year - 2016
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.7677
Subject(s) - gallic acid , adaptive neuro fuzzy inference system , inference system , peroxide value , propyl gallate , artificial neural network , antioxidant , chemistry , food science , fuzzy logic , biochemistry , computer science , artificial intelligence , fuzzy control system
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 ( k 1 ) and slope of propagation stage of oxidation curve ( k 2 ) and peroxide value at the IP ( PV IP ) 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, k 1 , k 2 and PV IP , 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. CONCLUSION 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