RESUMO
Objective:To establish the HPLC fingerprint method for assessing the quality of Moutan Cortex, and to determine the contents of paeonol, paeoniflorin, gallic acid, hydroxyl-paeoniflorin and benzoyl-paeoniflorin of Moutan Cortex in different growth period. Methods:Diamonsil Plus C18 column (250 mm × 4.6 mm, 5 μm) was used with the mobile phase comprising acetonitrile-0.05% formic acid solution and the flow rate of 1.0 ml/min with gradient elution manner. The detected wavelength was 230 nm for paeoniflorin and benzoyl-paeoniflorin, 267 nm for gallic acid, 258 nm for hydroxyl-paeoniflorin and 274 nm for paeonol with temperature column of 25 ℃. Then putting chromatograms into Similarity Evaluation System for Chromatographic Fingerprint of Chinese Materia Medica (2012A) to evaluate the similarity of Moutan Cortex in different growth period; then putting peak area data into SPSS software for cluster analysis and the clustering effect was determined. Results:The HPLC fingerprints established with this method has 23 shared peaks and 5 of them were identified, namely, paeonol, paeoniflorin, gallic acid, hydroxyl-paeoniflorin and benzoylpaeoniflorin. The similarity of Moutan Cortex in different years was between 0.850-0.991. This method has good linear relation ( r≥0.999 5), RSDs of precision, stability tests and reproducibility were lower than 1.6% ( n=6). Different growth periods of Moutan Cortex have obvious influence on the concentration of five compounds. Conclusion:This method is useful to evaluate and discriminate Moutan Cortex at different growth periods so as toprovide scientific reference on the harvest,industrialization and evaluation of Moutan Cortex.
RESUMO
Objective: To study the fingerprints of Paeoniae Radix Rubra in different habitats, and determine the content of five chemical components (gallic acid, albiflorin, paeoniflorin, benzoic acid, and benzoyl paeoniflorin) and systematically cluster them. The relationship between origin and content was analyzed by grey correlation degree to provide reference for the quality evaluation of Paeoniae Radix Rubra. Methods: Fingerprints of Paeoniae Radix Rubra from 21 different producing areas were constructed by high performance liquid chromatography. The results were classified by principal component analysis and systematic cluster analysis. The gray correlation degree method was used to process the index components and their relative correlations were calculated. Results: HPLC fingerprints of Paeoniae Radix Rubra from 21 habitats were established, 11 common peaks were confirmed, and five of them (gallic acid, albiflorin, paeoniflorin, benzoic acid, and benzoyl paeoniflorin) were identified. The similarity of Paeonia lactiflora was greater than 0.9, and the similarity of Paeonia veitchii was less than 0.9. It was divided into two categories by principal component analysis combined with cluster analysis. The results of grey correlation analysis showed that the relative correlation (ri) was the largest in Gansu, followed by Ganzi in Sichuan. Conclusion: There is a big difference in the relative yield of Paeoniae Radix Rubra in different habitats. This experiment provides a scientific basis for the quality evaluation of Paeoniae Radix Rubra by fingerprint analysis, principal component analysis combined system cluster analysis and grey correlation analysis method.