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Year : 2017  |  Volume : 13  |  Issue : 49  |  Page : 180-187

Combinative method using multi-components quantitation and HPLC fingerprint for comprehensive evaluation of Gentiana crassicaulis

1 College of Chemistry, Leshan Normal University, Leshan, P.R.China
2 College of Agronomy, Sichuan Agriculture University, Wenjiang, P.R.China

Correspondence Address:
Prof. Xingfu Chen
Sichuan Agriculture University, Wenjiang
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1296.197639

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Background: Gentiana crassicaulis () is an important traditional Chinese herb. Like other herbs, its chemical compounds vary greatly by the environmental and genetic factors, as a result, the quality is always different even from the same region, and therefore, the quality evaluation is necessary for its safety and effective use. In this study, a comprehensive method including HPLC quantitative analysis and fingerprints was developed to evaluate the quality of Cujingqinjiao and to classify the samples collected from Lijiang City of Yunnan province. A total of 30 common peaks including four identified peaks, were found, and were involved for further characterization and quality control of Cujingqinjiao. Twenty-one batches of samples from Lijiang City of Yunnan Province were evaluated by similarity analysis (SA), hierarchical cluster analysis (HCA), principal component analysis (PCA) and factor analysis (FA) according to the characteristic of common peaks.Results: The obtained data showed good stability and repeatability of the chromatographic fingerprint, similarity values were all more than 0.90. This study demonstrated that a combination of the chromatographic quantitative analysis and fingerprint offered an efficient way to quality consistency evaluation of Cujingqinjiao. Consistent results were obtained to show that samples from a same origin could be successfully classified into two groups.Conclusion: This study revealed that the combinative method was reliable, simple and sensitive for fingerprint analysis, moreover, for quality control and pattern recognition of Cujingqinjiao. Abbreviations used: SA: Similarity analysis, HCA: Hierarchical cluster analysis, PCA :Principal component Analysis, FA :Factor analysis

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