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HPLC Separation of Isoquinoline Alkaloids for Quality Control of Corydalis species

Authors
Kim, Eun KyungJeong, Eun KyungHan, Sang BeomJung, Jee H.Hong, Jongki
Issue Date
20-Oct-2011
Publisher
KOREAN CHEMICAL SOC
Keywords
Corydalis species; Alkaloids; Elution patterns; HPLC-UV
Citation
BULLETIN OF THE KOREAN CHEMICAL SOCIETY, v.32, no.10, pp 3597 - 3602
Pages
6
Journal Title
BULLETIN OF THE KOREAN CHEMICAL SOCIETY
Volume
32
Number
10
Start Page
3597
End Page
3602
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/21172
DOI
10.5012/bkcs.2011.32.10.3597
ISSN
0253-2964
1229-5949
Abstract
A simple and rapid analytical method was developed for the determination of eight isoquinoline alkaloids in Corydalis species. Eight isoquinoline alkaloids, including 2 aporphine alkaloids (isocorydine and glaucine) and 6 protoberberine alkaloids (coptisine, palmatine, berberine, canadine, corydaline, and tetrahydrocoptisine) were used as chemical markers which have a various biological activity and determined for quality control of Corydalis (C.) species (C. ternata, C. yanhusuo, and C. decumbens). To evaluate the quality of these herbal medicines, LC chromatographic separation of alkaloids were preferentially investigated on reversed-phase C18 column with pH variation and composition of mobile phase. In addition, the separation of these alkaloids in herbal extracts was found to be significantly affected on mobile phase composition using gradient elution. Especially for C. yanhusuo extract, berberine was seriously interfered with other alkaloid extracted from sample matrix when mobile phase composition was not optimized. As results, these compounds were successfully separated within 28 min using 10 mM ammonium acetate containing 0.2% triethylamine (adjusted at pH 5.0) as a mobile phase with gradient elution. On the basis of optimized HPLC conditions, 23 different Corydalis species samples were analyzed for the determination of alkaloid levels. In addition, principal component analysis (PCA) combined with the chromatographic data could be successfully classified the different geographic origin samples.
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