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KMID : 0861020160310030029
Korea Journal of Herbology
2016 Volume.31 No. 3 p.29 ~ p.35
Quality Assessment of Ijung-tang Preparations Using a HPLC Analysis
Ha Woo-Ram

Park Jin-Hyung
Yun Dong-In
Lee Jang-Cheon
Kim Jung-Hoon
Abstract
Objectives : Ijung-tang (IJT) is a traditional herbal formula and has been used to treat digestive diseases such as abdominal pain, vomiting, and diarrhea. IJT consists of four herbal medicines, Ginseng radix, Atractylodis rhizoma alba, Zingiberis rhizoma, and Glycyrrhizae radix et rhizoma, containing various bioactive compounds. Quality assesment of IJT preparations was performed by analytical method for determining marker compounds.

Methods : Determination of seven marker compounds in IJT preparations was quantitatively conducted by high- performance liquid chromatography equipped with a diode-array detector. The marker compounds were separated on a reversed-phase C18 column and the analytical method was successfully validated. Chemometric analysis was performed to compare IJT water extracts and commercial IJT granules.

Results : Limit of detection and limit of quantification values were in the ranges of 0.093.2.649 ¥ìg/mL and 0.283.8.027 ¥ìg/mL, respectively. Precisions were 0.30.3.87% within a day and 0.23.2.35% over three consecutive days. Recoveries of the marker compounds ranged from 87.35-107.05%, with relative standard deviation (RSD) values < 6.15%. Repeatabilities were < 1.20% and < 1.71% of RSD value for retention time and absolute peak area, respectively. The results from quantitative analysis showed that the quantities of seven marker compounds of IJT samples varied, as were found in principal component analysis and hierarchical clustering analysis.

Conclusions : The analytical method developed in the present study was precise and reliable to simultaneously determine marker compounds of IJT. Therefore, it can be used for the quality assessment of IJT preparations.
KEYWORD
Ijung-tang, Quantitative analysis, Method development, Principal component analysis, Hierarchical clustering analysis
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