Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis.
Detection of Adulteration in Canola Oil by Using GC-IMS and Chemometric Analysis.
复制标题
使用 GC-IMS 和化学计量分析检测菜籽油中的掺假
DOI：
10.1155/2018/3160265
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发表时间：
2018
影响因子：
1.8
通讯作者：
Chen B
中科院分区：
文献类型：
--
作者：
Chen T;Chen X;Lu D;Chen B
本研究的目的是检测菜籽油与其他植物油如向日葵油、大豆油和花生油的掺假情况，并建立预测菜籽油中掺杂油含量的模型。采用气相色谱-离子迁移率光谱分析(GC-IMS)和化学计量学分析相结合的方法对147个掺假样品进行检测，并结合方向梯度直方图(HOG)和多向主成分分析(MPCA)两种特征提取方法对数据集进行预处理。用典型判别分析(CDA)算法评价结果表明，HOG-MPCA-CDA模型对菜籽油掺入其他油品的鉴别和对不同掺杂量的菜籽油进行准确分类是可行的。利用偏最小二乘法建立了菜籽油掺假油位的预测模型。用偏最小二乘法建立的掺假预测模型具有较好的回归系数(R2>；0.95)和较低的误差(RMSE、≤、3.23)，具有较高的预测精度。
The aim of the present study was to detect adulteration of canola oil with other vegetable oils such as sunflower, soybean, and peanut oils and to build models for predicting the content of adulterant oil in canola oil. In this work, 147 adulterated samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and chemometric analysis, and two methods of feature extraction, histogram of oriented gradient (HOG) and multiway principal component analysis (MPCA), were combined to pretreat the data set. The results evaluated by canonical discriminant analysis (CDA) algorithm indicated that the HOG-MPCA-CDA model was feasible to discriminate the canola oil adulterated with other oils and to precisely classify different levels of each adulterant oil. Partial least square analysis (PLS) was used to build prediction models for adulterant oil level in canola oil. The model built by PLS was proven to be effective and precise for predicting adulteration with good regression (R2>0.95) and low errors (RMSE ≤ 3.23).
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