Chemometric tools for food fraud detection: The role of target class in non-targeted analysis
Chemometric tools for food fraud detection: The role of target class in non-targeted analysis
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DOI：
10.1016/j.foodchem.2020.126448
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发表时间：
2020-07-01
期刊：
影响因子：
8.8
通讯作者：
Pomerantsev, A. L.
中科院分区：
文献类型：
--
作者：
Rodionova, O. Ye;Pomerantsev, A. L.
采用判别分析和单类分类器分析了非靶向分析在食品欺诈检测中应用的化学计量学问题。探讨了两种方法的异同。分类结果的特征在于一组指数称为品质因数。它们全面说明了分类的质量和可靠性。用牛至药材掺假的实际例子说明了这一原理。近红外光谱的信息区9000-4000 cm（-1）用作分析手段。牛至数据收集的每种方法的应用程序的结果。结果表明，判别式方法只是部分适合解决认证问题。一类分类器是一个功能强大的，专门用于非目标性分析。本文介绍的逐步分析方法也可成功地应用于各种食品的食品安全性的揭示。
The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit. They comprehensively characterized the quality and reliability of classification. The principle is illustrated using an actual example of Oregano herbs adulteration. The informative region 9000-4000 cm(-1) of near-Infrared spectroscopy is used as analytical means. The results of the application of each method for Oregano data collection are presented. It is shown that the discriminant method is only partially appropriate for solving the authentication problem. One class classifier is a powerful and devoted for non-targeted analysis. The step by step analysis introduced in the paper can also be successfully utilized in apply for revealing of forgeries of various food products.