Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil

In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oi...

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Autores principales:	Shengquan Huang, Ying Liu, Xuyuan Sun, Jinwei Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	free radical electron paramagnetic resonance volatile lipid oxidation artificial neural network (ANN) Organic chemistry QD241-441
Acceso en línea:	https://doaj.org/article/863f04a83fce40cbbf6848443223c589
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spelling	oai:doaj.org-article:863f04a83fce40cbbf6848443223c5892021-11-11T18:39:34ZApplication of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil10.3390/molecules262167171420-3049https://doaj.org/article/863f04a83fce40cbbf6848443223c5892021-11-01T00:00:00Zhttps://www.mdpi.com/1420-3049/26/21/6717https://doaj.org/toc/1420-3049In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (<i>E</i>)-2-decenal, (<i>E</i>,<i>E</i>)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil.Shengquan HuangYing LiuXuyuan SunJinwei LiMDPI AGarticlefree radicalelectron paramagnetic resonancevolatilelipid oxidationartificial neural network (ANN)Organic chemistryQD241-441ENMolecules, Vol 26, Iss 6717, p 6717 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	free radical electron paramagnetic resonance volatile lipid oxidation artificial neural network (ANN) Organic chemistry QD241-441
spellingShingle	free radical electron paramagnetic resonance volatile lipid oxidation artificial neural network (ANN) Organic chemistry QD241-441 Shengquan Huang Ying Liu Xuyuan Sun Jinwei Li Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil
description	In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (<i>E</i>)-2-decenal, (<i>E</i>,<i>E</i>)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil.
format	article
author	Shengquan Huang Ying Liu Xuyuan Sun Jinwei Li
author_facet	Shengquan Huang Ying Liu Xuyuan Sun Jinwei Li
author_sort	Shengquan Huang
title	Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil
title_short	Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil
title_full	Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil
title_fullStr	Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil
title_full_unstemmed	Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil
title_sort	application of artificial neural network based on traditional detection and gc-ms in prediction of free radicals in thermal oxidation of vegetable oil
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/863f04a83fce40cbbf6848443223c589
work_keys_str_mv	AT shengquanhuang applicationofartificialneuralnetworkbasedontraditionaldetectionandgcmsinpredictionoffreeradicalsinthermaloxidationofvegetableoil AT yingliu applicationofartificialneuralnetworkbasedontraditionaldetectionandgcmsinpredictionoffreeradicalsinthermaloxidationofvegetableoil AT xuyuansun applicationofartificialneuralnetworkbasedontraditionaldetectionandgcmsinpredictionoffreeradicalsinthermaloxidationofvegetableoil AT jinweili applicationofartificialneuralnetworkbasedontraditionaldetectionandgcmsinpredictionoffreeradicalsinthermaloxidationofvegetableoil
_version_	1718431772665970688

Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil

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