| Literature DB >> 19757357 |
Hong Yang1, Chenxi Zhao, Xiaomei Wang, Yizeng Liang, Yingxu Zeng, Hai Wu, Qingsong Xu, Huiying Lv.
Abstract
The quality assessment and control of traditional Chinese medicines (TCM) has received a great deal of attention worldwide with its tremendous increasing use. Chromatographic fingerprinting is thought to be a good approach for this task and has been used for the quality assessment and control of many herbal medicines. However, there are only a few reports on the quality control of TCM preparation by chromatographic fingerprinting. In the present work, gas chromatography-mass spectrometry (GC-MS) combined with chemometric methods were used for the chromatographic fingerprint analysis and characterization of Shengui hair-growth tincture (SGHGT), which is a complex TCM prescription made from 9 herbs. Thirteen "common peaks" were identified by MS and a comparison of retention indices. The software "The Traditional Chinese Medicine Quality Control System 1.0" (TCMQCS, developed by Research Center of Modernization of Chinese Herbal Medicine, Central South University) was used to evaluate the similarities. Principal component analysis (PCA) was used for the classification of 23 batches of SGHGT samples provided by Hunan Fusheng Hairgrowth Pharmaceutical Factory. The 23 batches of samples made in different years had similar GC-MS fingerprints. Four clusters were obtained from PCA treatment according to their production year. The proposed method was validated in precision and repeatability through the calculation of relative retention times and relative peak areas of the 13 common compounds to the reference compound eugenol. The result indicated that the method is feasible and applicable for the quality control of SGHGT. SUPPORTING INFORMATION available online at http://www.thieme-connect.de/ejournals/toc/plantamedica. Copyright Georg Thieme Verlag KG Stuttgart New York.Entities:
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Year: 2009 PMID: 19757357 DOI: 10.1055/s-0029-1186140
Source DB: PubMed Journal: Planta Med ISSN: 0032-0943 Impact factor: 3.352