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HPLC Separation of Isoquinoline Alkaloids for Quality Control of Corydalis species

  • Kim, Eun-Kyung (College of Pharmacy & Kyung Hee East-West Pharmaceutical Research Institute, Kyung Hee University) ;
  • Jeong, Eun-Kyung (College of Pharmacy & Kyung Hee East-West Pharmaceutical Research Institute, Kyung Hee University) ;
  • Han, Sang-Beom (College of Pharmacy, Choongang University) ;
  • Jung, Jee-H. (College of Pharmacy, Pusan National University) ;
  • Hong, Jong-Ki (College of Pharmacy & Kyung Hee East-West Pharmaceutical Research Institute, Kyung Hee University)
  • Received : 2011.05.12
  • Accepted : 2011.08.06
  • Published : 2011.10.20

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.

Keywords

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