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Quantitative and Pattern Recognition Analyses for the Quality Evaluationof Herba Epimedii by HPLC
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  • Quantitative and Pattern Recognition Analyses for the Quality Evaluationof Herba Epimedii by HPLC
  • Quantitative and Pattern Recognition Analyses for the Quality Evaluationof Herba Epimedii by HPLC
저자명
Nurul Islam. M.,Lee. Sang-Kyu,Jeong. Seo-Young,Kim. Dong-Hyun,Jin. Chang-Bae,Yoo. Hye-Hyun
간행물명
Bulletin of the Korean Chemical Society
권/호정보
2009년|30권 1호|pp.137-144 (8 pages)
발행정보
대한화학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

In this study, quantitative and pattern recognition analyses for the quality evaluation of Herba Epimedii using HPLC was developed. For quantitative analysis, five major bioactive constituents, hyperin, epimedin A, epimedin B, epimedin C, and icariin were determined. Analysis was carried out on Capcell pak $C_{18}$ column ($250{ ime}4.6$ mm, 5 ${mu}m$) with a mobile phase of mixture of acetonitrile and 0.1% formic acid, using UV detection at 270 nm. The linear behavior was observed over the investigated concentration range (2-50 ${mu}g/mL;;r_2;>$ 0.99) for all analytes. The intraand inter-day precisions were lower than 4.3% (as a relative standard deviation, RSD) and accuracies between 95.1% and 104.4%. The HPLC analytical method for pattern recognition analysis was validated by repeated analysis of one reference sample. The RSD of intra- and inter-day variation of relative retention time (RRT) and relative peak area (RPA) of the 12 selected common peaks were below 0.8% and 4.7%, respectively. The developed methods were applied to analysis of twenty Herba Epimedii extract samples. Contents of hyperin, epimedin A, epimedin B, epimedin C, and icariin were calculated to be 0$sim$0.79, 0.69$sim$1.91, 0.93$sim$9.58, 0.65$sim$3.05, and 2.43$sim$11.8 mg/g dried plant. Principal component analysis (PCA) showed that most samples were clustered together with the reference samples but several apart from the main cluster in the PC score plot, indicating differences in overall chemical composition between two clusters. The present study suggests that quantitative determination of marker compounds combined with pattern-recognition method can provide a comprehensive approach for the quality assessment of herbal medicines.