Fast identification of the geographical origin of Gastrodia elata using excitation-emission matrix fluorescence and chemometric methods

被引:36
作者
Long, Wan-Jun [1 ]
Wu, Hai-Long [1 ]
Wang, Tong [1 ]
Dong, Ming-Yue [1 ]
Chen, Lu-Zhu [1 ]
Yu, Ru-Qin [1 ]
机构
[1] Hunan Univ, Coll Chem & Chem Engn, State Key Lab Biosensing & Chemometr, Changsha 410082, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Gastrodia elata; Excitation-emission matrix fluorescence; Chemometric; Geographical origin; SPECTROSCOPY; DISCRIMINATION; CALIBRATION; CLASSIFICATION; ADULTERATION; BL; QUANTIFICATION; COMPONENTS; SAMPLES; 3-WAY;
D O I
10.1016/j.saa.2021.119798
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLSDA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLSDA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines. (c) 2021 Elsevier B.V. All rights reserved.
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页数:7
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