Issue 26, 2022, Issue in Progress

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with chemometrics to identify the origin of Chinese medicinal materials

Abstract

Geographical origin and authenticity are two core factors to promote the development of traditional Chinese medicine (TCM) herbs perception in terms of quality and price. Therefore, they are important to both sellers and consumers. Herein, we propose an efficient, accurate method for discrimination of genuine and non-authentic producing areas of TCM by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Take Atractylodes macrocephala Koidz (AMK) of compositae as an example, the MALDI-TOF MS spectra data of 120 AMK samples aided by principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA) and random forest (RF) successfully differentiated Zhejiang province, Anhui province and Hunan province AMK according to their geographical location of origin. The correct classification rates of test set were above 93.3%. Furthermore, 5 recollected AMK samples were used to verify the performance of the classification models. The outcome of this study can be a good resource in building a database for AMK. The combined utility of MALDI-TOF MS and chemometrics is expected to be expanded and applied to the origin traceability of other TCMs.

Graphical abstract: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with chemometrics to identify the origin of Chinese medicinal materials

Supplementary files

Article information

Article type
Paper
Submitted
30 Mar 2022
Accepted
25 May 2022
First published
07 Jun 2022
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2022,12, 16886-16892

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with chemometrics to identify the origin of Chinese medicinal materials

H. Fang, Y. Chen, H. Wu, Y. Chen, T. Wang, J. Yang, H. Fu, X. Yang, X. Li and R. Yu, RSC Adv., 2022, 12, 16886 DOI: 10.1039/D2RA02040H

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