Quality evaluation of total flavonoids of Lamiophlomis rotata from different producing areas / 中国药房

ABSTRACT

OBJECTIVE To evaluate the quality of total flavonoids of Lamiophlomis rotata from different producing areas. METHODS Total flavonoids of S1-S15 batches of L. rotata from different producing areas were extracted by percolation and purified by polyamide column. The content of total flavonoids was determined by UV spectrophotometry， and its purity was calculated. HPLC fingerprint chromatograms and control fingerprint of total flavonoids from 15 batches of L. rotata were established with Similarity Evaluation System of Chromatographic Fingerprint of TCM （2012 version）. Their similarities were analyzed. Cluster analysis， principal component analysis and orthogonal partial least squares-discriminant analysis were used to evaluate the quality of total flavonoids from 15 batches of L. rotata， and the main components which affected the quality of total flavonoids were analyzed. RESULTS The purities of total flavonoids from 15 batches of L. rotata were 55.82%-94.12%， with an average value of 77.72%； a total of five common peaks were identified in the fingerprint， and No. 3 peak was luteolin； the similarities between the fingerprint of each batch of samples and the control fingerprint were 0.925-1.000. By cluster analysis， S1 and S3-S9 were clustered into the first class， which were samples from Qinghai Province and Tibet Autonomous Region. S14 and S15 were clustered into the second class， which were samples from Yunnan Province. S10-S13 were clustered into the third class， which were all samples from Sichuan Province. S2 was clustered into the fourth class. The principal component analysis showed that the qualities of samples from the first and fourth classes were better； peaks 2， 3 and 5 were identified as the main components that caused the differences among different batches of samples by orthogonal partial least squares-discriminant analysis. CONCLUSIONS The qualities of total flavonoids of L. rotata from Qinghai Province and Tibet Autonomous Region are better.