Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil

文献类型： 外文期刊

第一作者： Zhang, Liangxiao

作者： Zhang, Liangxiao;Shuai, Qian;Li, Peiwu;Zhang, Qi;Ma, Fei;Zhang, Wen;Ding, Xiaoxia;Shuai, Qian;Zhang, Qi;Ma, Fei;Li, Peiwu;Zhang, Qi;Ma, Fei;Li, Peiwu;Ding, Xiaoxia;Zhang, Liangxiao;Li, Peiwu;Zhang, Wen;Zhang, Liangxiao

作者机构：

关键词： Ion mobility spectrometry fingerprints;Sesame oil;Adulteration detection;Rapid detection technology;Recursive support vector machine;Random forests

期刊名称：FOOD CHEMISTRY （ 影响因子：7.514； 五年影响因子：7.516 ）

ISSN： 0308-8146

年卷期： 2016 年 192 卷

页码：

收录情况： SCI

摘要： A simple and rapid detection technology was proposed based on ion mobility spectrometry (IMS) fingerprints to determine potential adulteration of sesame oil. Oil samples were diluted by n-hexane and analyzed by IMS for 20 s. Then, chemometric methods were employed to establish discriminant models for sesame oils and four other edible oils, pure and adulterated sesame oils, and pure and counterfeit sesame oils, respectively. Finally, Random Forests (RF) classification model could correctly classify all five types of edible oils. The detection results indicated that the discriminant models built by recursive support vector machine (R-SVM) method could identify adulterated sesame oil samples (>= 10%) with an accuracy value of 94.2%. Therefore, IMS was shown to be an effective method to detect the adulterated sesame oils. Meanwhile, IMS fingerprints work well to detect the counterfeit sesame oils produced by adding sesame oil essence into cheaper edible oils. (C) 2015 Elsevier Ltd. All rights reserved.

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