Issue 5, 2015

Quantitative and chemical fingerprint analysis for quality evaluation of the dried bark of wild Phellodendron amurense Rupr. based on HPLC-DAD-MS combined with chemometrics methods

Abstract

The dried bark of Phellodendron amurense Rupr., known as “Guanhuangbo” in China, has been widely used as a traditional Chinese medicine for thousands of years. In this paper, an accurate and reliable method using high performance liquid chromatography coupled with diode array detection and mass spectrometry (HPLC-DAD-MS) was developed for the quality evaluation of wild Guanhuangbo. Six bioactive compounds, namely, chlorogenic acid, phellodendrine, magnoflorine, jatrorrhizine, palmatine and berberine, were determined simultaneously in 37 batches of wild Guanhuangbo samples collected from different locations in China. The chromatographic conditions and extraction procedures were optimized by an orthogonal design during the study and the identities of the compounds were confirmed by LC-MS. Moreover, similarity analysis and hierarchical clustering analysis were successfully applied to demonstrate the variability of these wild Guanhuangbo samples and the data obtained from different analysis showed good consistency. The results indicated that the developed multi-compound determination method in combination with fingerprint analysis is suitable for the quantitative analysis and quality evaluation of the dried bark of wild Phellodendron amurense Rupr.

Graphical abstract: Quantitative and chemical fingerprint analysis for quality evaluation of the dried bark of wild Phellodendron amurense Rupr. based on HPLC-DAD-MS combined with chemometrics methods

Supplementary files

Article information

Article type
Paper
Submitted
27 Nov 2014
Accepted
07 Jan 2015
First published
29 Jan 2015

Anal. Methods, 2015,7, 2041-2049

Author version available

Quantitative and chemical fingerprint analysis for quality evaluation of the dried bark of wild Phellodendron amurense Rupr. based on HPLC-DAD-MS combined with chemometrics methods

Y. Zhang, Z. Zhang, H. Liu, B. Zhang, Y. Liao and Z. Zhang, Anal. Methods, 2015, 7, 2041 DOI: 10.1039/C4AY02827A

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