Issue 39, 2016

Establishment and validation of quantitative analysis of multi-components by a single marker for quality control of polygoni multiflori radix

Abstract

Quality control of Traditional Chinese Medicine (TCM) is one of the biggest challenges facing its clinical practice. The quality of Polygoni Multiflori Radix (PM) is mainly attributed to the content of stilbenes and anthraquinones. Herein, a sensitive, precise, and specific high-performance liquid chromatography (HPLC) method is established for the quantification of the six major components in PM, which include cis-2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside (cis-TSG), trans-2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside (trans-TSG), emodin-8-O-β-D-glucoside, physcion-8-O-β-D-glucoside, emodin and physcion. Based on this, using trans-TSG as the internal reference, quantitative analysis of multi-components by a single marker (QAMS) was carried out, and relative correction factors (RCFs) were calculated to determine the contents of the other five components. Tested by a robustness experiment under certain changeable conditions, the RCFs proved to be stable with RSDs below 5.0%. Moreover, on comparing the contents of the target components determined by an external standard method (EMS) and QAMS, there were no significant differences observed between the two methods with absolute relative errors of <5%. The abovementioned results indicate that QAMS possesses high convenience, accuracy, and feasibility and can be successfully applied to PM and its preparations for multi-component quality control.

Graphical abstract: Establishment and validation of quantitative analysis of multi-components by a single marker for quality control of polygoni multiflori radix

Article information

Article type
Paper
Submitted
06 Jul 2016
Accepted
30 Aug 2016
First published
30 Aug 2016

Anal. Methods, 2016,8, 7170-7176

Establishment and validation of quantitative analysis of multi-components by a single marker for quality control of polygoni multiflori radix

D. Gao, Y. Li, N. Wang, C. Li, C. Zhang, Z. Bai, X. Xiao, W. Gao and J. Wang, Anal. Methods, 2016, 8, 7170 DOI: 10.1039/C6AY01921H

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