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http://dx.doi.org/10.5012/bkcs.2009.30.10.2287

Quality Assessment of Curcuma longa L. by Gas Chromatography-Mass Spectrometry Fingerprint, Principle Components Analysis and Hierarchical Clustering Analysis  

Li, Ming (The Research Center for Quality Control of Natural Medicine, Guizhou Normal University)
Zhou, Xin (The Research Center for Quality Control of Natural Medicine, Guizhou Normal UniversityKey Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment)
Zhao, Yang (The Research Center for Quality Control of Natural Medicine, Guizhou Normal University,Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment)
Wang, Dao-Ping (The Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academy of Sciences)
Hu, Xiao-Na (The Research Center for Quality Control of Natural Medicine, Guizhou Normal University)
Publication Information
Bulletin of the Korean Chemical Society / v.30, no.10, 2009 , pp. 2287-2293 More about this Journal
Abstract
Gas Chromatography-Mass Spectrometry (GC-MS) fingerprint analysis, Principle Components Analysis (PCA), and Hierarchical Cluster Analysis (HCA) were introduced for quality assessment of Curcuma longa L. (C. longa). The GC-MS fingerprint method was developed and validated by analyzing 33 batches of samples of C. longa from different geographic locations. 18 chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression. Two principal components (PCs) were extracted by PCA. C. longa collected from Guizhou and Fujian were separated from other samples by PC1, capturing 71.83% of variance. While, PC2 contributed for their further separation, capturing 11.13% of variance. HCA confirmed the result of PCA analysis. Therefore, GC-MS fingerprint study with chemometric techniques provides a very flexible and reliable method for quality assessment of C. longa.
Keywords
Fingerprint; Curcuma longa L.; GC-MS; Principle components analysis; Hierachical clustering analysis;
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