Combinative Method Using Multi-components Quantitation and HPLC Fingerprint for Comprehensive Evaluation of Gentiana crassicaulis

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. SUMMARY HPLC quantitative analysis and fingerprints was developed to evaluate the quality of Gentiana crassicaulisSimilarity analysis, hierarchical cluster analysis, principal component analysis and factor analysis were employed to analysis the chromatographic dataset.The results of multi-components quantitation analysis, similarity analysis, hierarchical cluster analysis, principal component analysis and factor analysis were consistent.All samples could be classified into two groups, which could to some extent reflect the quality differences of theses samples. Abbreviations used: SA: Similarity analysis, HCA: Hierarchical cluster analysis, PCA :Principal component Analysis, FA :Factor analysis.