期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 161, 期 -, 页码 147-150出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2016.11.009
关键词
One-class support vector machines (OC-SVM); Multivariate adulteration detection; Untargeted adulteration; Sesame oil; Chemometrics
类别
资金
- Project of National Science & Technology Pillar Plan [2012BAK08B03]
- National Major Project for Agro-product Quality & Safety Risk Assessment [GJFP2016001, GJFP2016015]
- National Natural Science Foundation of China [21205118]
- earmarked fund for China Agriculture Research System [CARS-13]
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.
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