Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil.
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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基于传统检测和GC-MS的人工神经网络在植物油热氧化中的自由基预测中应用。
DOI：
10.3390/molecules26216717
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发表时间：
2021-11-06
期刊：
影响因子：
--
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Huang S;Liu Y;Sun X;Li J
采用电子顺磁共振（EPR）和气相色谱-质谱（GC-MS）联用技术研究了4种油脂在热处理过程中脂质自由基和氧化挥发产物的变化。EPR结果表明，亚麻籽油（LO）的信号强度最高，其次是向日葵籽油（SO）、菜籽油（RO）和棕榈油（PO）。此外，四种油的信号强度随加热时间而增加。GC-MS分析表明，氧化油的主要挥发性成分为（E）-2-癸烯醛、（E，E）-2，4-癸二烯醛和2-十一烯醛。此外，氧化后的PO和LO的挥发物含量分别最高和最低。根据油品特性，建立了自由基的人工神经网络智能评价模型。人工神经网络模型的决定系数（R2）均大于0.97，预测值与真实值相差不大，表明油液剖面与化学计量学相结合可以准确预测热氧化油的自由基。
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 (E)-2-decenal, (E,E)-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.
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影响因子：
8.1
作者：
Martinez-Yusta, Andrea;Guillen, Maria D.
通讯作者：
Guillen, Maria D.
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影响因子：
8.8
作者：
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通讯作者：
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影响因子：
7.4
作者：
Qian, SY;Wang, HP;Buettner, GR
通讯作者：
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影响因子：
9.5
作者：
Duyen Thi Do;Thanh Quynh Trang Le;Nguyen Quoc Khanh Le
通讯作者：
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影响因子：
4
作者：
Nguyen Quoc Khanh Lee;Huynh, Tuan-Tu
通讯作者：
Huynh, Tuan-Tu