Chemical discrimination of cortex Phellodendri amurensis and cortex Phellodendri chinensis by multivariate analysis approach

Articles

Abstract
Pharmacognosy Magazine,2016,12,45,41-49.
Published:February 2016
Type:Original Article
Authors:
Author(s) affiliations:

Hui Sun, Huiyu Wang, Aihua Zhang, Guangli Yan, Ying Han, Yuan Li, Xiuhong Wu, Xiangcai Meng, Xijun Wang
Department of Pharmaceutical Analysis, National TCM Key Laboratory of Serum Pharmacochemistry, Laboratory of Metabolomics and Chinmedomics, Heilongjiang University of Chinese Medicine, Harbin 150040, China

Abstract:

Background: As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. Materials and Methods: In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Results: Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. Conclusion: The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment.

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