Identification of the Geographic Origin of Peanut Kernels by Raman Spectroscopy Fingerprinting with Chemometrics

文献类型： 外文期刊

第一作者： Sun, Tianjia

作者： Sun, Tianjia;Yang, Qingli;Guo, Yichen;Zhao, Haiyan;Zhang, Yingquan;Guo, Boli;Jia, Qi

作者机构：

关键词： k-nearest neighbor (k-NN); peanut kernels; Raman spectroscopy; stepwise linear discriminant analysis (SLDA); support vector machine (SVM)

期刊名称：ANALYTICAL LETTERS （ 影响因子：2.0； 五年影响因子：1.8 ）

ISSN： 0003-2719

年卷期： 2023 年

页码：

收录情况： SCI

摘要： This study aimed to investigate the feasibility of identifying the geographical origin of peanuts by combining Raman spectroscopy with chemometrics. A total of 161 peanut samples were collected from Jilin, Jiangsu, and Shandong provinces in China, and their Raman spectra were collected. One-way analysis of variance (ANOVA) was used to analyze the difference in characteristic Raman spectra of peanuts from these locations. Raman spectroscopy combined with principal component analysis (PCA), k-nearest neighbor (k-NN), stepwise linear discriminant analysis (SLDA), and support vector machines (SVM) were used to classify the peanuts by province and Jilin Province city. One-way ANOVA indicated that the peak intensities at 2900, 1660, 1440, 1077, and 848 cm(-1) had significant differences. The peaks at 2900, 1660, 1440, 1300, and 1077 cm(-1) had significant differences in the Jilin Province city. The correct identification rates were highest for k-NN. This study demonstrates the identification of the origin of peanuts by Raman spectroscopy with chemometrics and may provide technical support for the traceability of other agricultural products.

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