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Application of FT-IR spectroscopy and chemometric technique for the identification of three different parts of Camellia nitidissima and discrimination of its authenticated product

文献类型： 外文期刊

作者： Tew, Wan Yin 1 ; Ying, Chen 1 ; Zhang, Wujun 1 ; Liu, Baocai 1 ; Yoon, Tiem Leong 4 ; Yam, Mun Fei 2 ; Chen, Jingying 1 ;

作者机构： 1.Fujian Acad Agr Sci, Inst Agr Bioresource, Res Ctr Med Plant, Fuzhou, Peoples R China

2.Univ Sains Malaysia, Sch Pharmaceut Sci, Gelugor, Malaysia

3.Beijing Univ Chinese Med, Sch Chinese MateriaMed, Beijing, Peoples R China

4.Univ Sains Malaysia, Sch Phys, Gelugor, Malaysia

5.Fujian Univ Tradit Chinese Med, Coll Pharm, Fuzhou, Peoples R China

关键词： golden camellia; Fourier transform infrared spectroscopy; second-derivative infrared spectra; two-dimensional correlation infrared spectra; principal component analysis; orthogonal partial least square discriminant analysis

期刊名称：FRONTIERS IN PHARMACOLOGY （ 影响因子：5.988； 五年影响因子：6.455 ）

ISSN：

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Camellia nitidissima C.W. Chi is a golden camellia recognized in Chinese herbology and widely used as tea and essential oil in Chinese communities. Due to its diverse pharmacological properties, it can be used to treat various diseases. However, unethical sellers adulterated the flower with other parts of Camellia nitidissima in their product. This study used an integrated tri-step infrared spectroscopy method and a chemometric approach to distinguish C. nitidissima's flowers, leaves, and seeds. The three different parts of C. nitidissima were well distinguished using Fourier transform infrared spectroscopy (FT-IR), second-derivative infrared (SD-IR) spectra, and two-dimensional correlation infrared (2D-IR) spectra. The FT-IR and SD-IR spectra of the samples were subjected to principal component analysis (PCA), PCA-class, and orthogonal partial least square discriminant analysis (OPLS-DA) for classification and discrimination studies. The three parts of C. nitidissima were well separated and discriminated by PCA and OPLS-DA. The PCA-class model's sensitivity, accuracy, and specificity were all > 94%, indicating that PCA-class is the good model. In addition, the RMSEE, RMSEP, and RMSECV values for the OPLS-DA model were low, and the model's sensitivity, accuracy, and specificity were all 100%, showing that it is the excellent one. In addition, PCA-class and OPLS-DA obtained scores of 27/32 and 26/32, respectively, for detecting adulterated and other TCM reference flower samples from C. nitidissima. Combining an infrared spectroscopic method with a chemometric approach proved that it is possible to differentiate distinct sections of C. nitidissima and discriminate adulterated samples of C.nitidissima flower.

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