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Citation: GONG Dandan, DONG Jiajun, SUN Guoxiang, ZHANG Jing, ZHANG Yujing, SUN Wanyang. A novel pattern for predicting the quality of Chinese herba preparation intelligently by high performance liquid chromatographic formula fingerprints[J]. Chinese Journal of Chromatography, ;2017, 35(6): 643-649. doi: 10.3724/SP.J.1123.2016.12041 shu

A novel pattern for predicting the quality of Chinese herba preparation intelligently by high performance liquid chromatographic formula fingerprints

1.
College of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China;
2.
Institute of Traditional Chinese Medicine & Natural Products, College of Pharmacy, Jinan University, Guangzhou 510632, China

Received Date: 27 December 2016

Fund Project: National Natural Science Foundation of Personnel Training Funds (No. J1103606).

In order to build the fusion models, the high performance liquid chromatographic (HPLC) fingerprints of scutellariae radix (SR), rhei radix et rhizoma (RRR), coptidis rhizoma (CR) and their synthesizing fingerprints were developed in this study. After exploring the consistency between the fingerprints of compound synthesizing fingerprints (CSF) and the sample, the quality of traditional Chinese medicine preparation was predicted intelligently using CSF. HPLC coupled with diode array detector was used to obtain chromatograms of SR, RRR, CR and Yi Qing Tablet (YQT) samples at 268 nm. Meanwhile, the quality of CSF and the 15 batches of YQT samples was evaluated by systematically quantified fingerprint method (SQFM) qualitatively and quantitatively. The chromatograms showed that CSF covered the main fingerprints' information of each herb and the 55 common peaks of CSF covered the main information of the 50 common peaks in YQT sample. The evaluation results showed that among the 15 batches of YQT samples, only YQT-S01 was grade 5 and the others were all above grade 3. Most of the CSFs were grade 2 or grade 1 except CSF-2 which was grade 6. The fingerprints of Chinese herba preparation could be replaced by CSF to achieve a novel pattern for predicting the quality of TCM preparation intelligently by studying the relationship between the standard fingerprints and the CSF, and simultaneously developing first-class evaluation software.
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