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 )
Authors: Maryam Asnaashari , Reza Farhoosh , Reza Farahmandfar ,
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Abstract
BACKGROUND: As a result of concerns regarding possible health hazards of synthetic antioxidants, gallic acid andmethyl gallate may be introduced as natural antioxidants to improve oxidative stability ofmarine 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 typeofantioxidant (gallic acidandmethylgallate), temperature (35,45and55 ∘C)andconcentration (0, 200, 400, 800 and 1600mgL−1) and four outputs containing induction period (IP), slope of initial stage of oxidation curve (k1) andslope of propagation stage ofoxidation curve (k2) andperoxide value at the IP (PVIP)wereperformedto predict theoxidation parameters of Kilka oil triacylglycerols and were compared to multiple linear regression (MLR). RESULTS: The results showed ANFISwas the bestmodel with high coefficient of determination (R2 =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 bestMLP 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.
Keywords
ANFIS; Artificial neural network; Gallic acid; Kilka fish oil; Lipid oxidation; Methyl gallate; MLR; Sensitivity analysis
@article{paperid:1057747,
author = {Maryam Asnaashari and Farhoosh, Reza and Reza Farahmandfar},
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},
journal = {Journal of the Science of Food and Agriculture},
year = {2016},
volume = {96},
number = {13},
month = {March},
issn = {0022-5142},
pages = {4594--4602},
numpages = {8},
keywords = {ANFIS; Artificial neural network; Gallic acid; Kilka fish oil; Lipid oxidation; Methyl gallate; MLR; Sensitivity analysis},
}
%0 Journal Article
%T 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
%A Maryam Asnaashari
%A Farhoosh, Reza
%A Reza Farahmandfar
%J Journal of the Science of Food and Agriculture
%@ 0022-5142
%D 2016
