Multivariate adulteration detection for sesame oil

文献类型： 外文期刊

第一作者： Zhang, Liangxiao

作者： Zhang, Liangxiao;Huang, Xiaorong;Li, Peiwu;Jiang, Jun;Mao, Jin;Ding, Xiaoxia;Zhang, Qi;Zhang, Qi;Li, Peiwu;Zhang, Qi;Zhang, Liangxiao;Li, Peiwu;Ding, Xiaoxia;Zhang, Liangxiao;Huang, Xiaorong;Li, Peiwu;Jiang, Jun;Mao, Jin;Ding, Xiaoxia;Na, Wei;Zhang, Liangxiao

作者机构：

关键词： One-class support vector machines (OC-SVM);Multivariate adulteration detection;Untargeted adulteration;Sesame oil;Chemometrics

期刊名称：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS （ 影响因子：3.491； 五年影响因子：3.839 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Multivariate and untargeted adulterations are real cases of oil adulteration in practice. In this study, one-class support vector machine (OC-SVM) was used to build the model for detecting multivariate and untargeted adulterations of sesame oil. The predictive model was subsequently validated by an independent test set. The results indicated that the OC-SVM model could completely detect the adulterated oils. Moreover, oils adulterated with different levels of mixed edible oils were simulated by Monte Carlo method and employed to determine the lowest adulteration level of the predictive model. Compared with earlier studies, the OC-SVM model proposed for sesame oil in this study is more robust to detect untargeted and multivariate adulteration.

分类号： TB9

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