[Qualitative and quantitative analysis of Rhodobryum giganteum by using nonlinear oscillating chemical fingerprint technique].

A new method for qualitative and quantitative analysis of Rhodobryum giganteum by using the nonlinear oscillating chemical was established for improving the quality control standard of R. giganteum. Its potential(E)/time(t) curve was recorded by electrochemical workstation in the oscillation reaction system of BrO~-_3-Ce(SO_4)_2-H_2SO_4-malonic acid/tartaric acid. The nonlinear oscillating chemical fingerprints were investigated for repeatability, and it was found that the RSD values of the four characteristic parameters of R. giganteum were less than 4.1%, indicating a good repeatability and high precision of this experiment. After optimizing the experimental parameters such as particle size, rotation speed and temperature, a new method based on nonlinear oscillating chemical was used for qualitative and quantitative analysis of R. giganteum. The results showed that there was a good linear relationship between the induction time/the period of oscillation and the dosage of herbs(0.1-1.1 g), with the relative coefficients of 0.978 and 0.975, respectively. Besides, the highest potential showed a nonlinear relationship with the dosage of herbs, with the relative coefficient of 0.999. This method was also used to discriminate the R. giganteum and R. roseum. They were similar in appearance, but their fingerprints were quite different. Independent sample t test results showed that there were significant differences in the oscillation time, the maximum amplitude and the induction time, providing a basis for the identification of the basic sources of Herba Rhodobryi Rasei.

A new strategy for choosing "Q-markers" via network pharmacology, application to the quality control of a Chinese medical preparation.

Due to its chemical complexity, proper quality control for a Chinese medical preparation (CMP) has been a great challenge. Choosing the appropriate quality markers (Q-markers) for quality control of CMP is an important work. Best of all, the chosen Q-markers are the main chemical compounds from the herbals as well as the active constituents of this CMP. Only in this way the established quality control system can really achieve the purpose of controlling the quality of CMP and ensuring the safely and effectively use of CMP. To achieve the purpose, network pharmacology combined with the contents of chemical compounds in the CMP has been used in this research. We took an anti-arrhythmic CMP, Shenxian-Shengmai oral liquid (SSOL), as an example. Firstly, UPLC-QTOF-MS/MS method was used to analyze the main components of SSOL. A total of 64 compounds were unambiguously or tentatively identified and 32 of them were further validated by reference compounds. Secondly, the network was constructed based on the identified compounds to predict the effective compounds related to cardiac arrhythmias. Based on the existing database and the operation method of topology, a method of double network analysis (DNAA) was proposed, from which 10 important targets in the pathway of arrhythmia were screened out, and 26 compounds had good antiarrhythmic activity. Based on the prediction results of network pharmacology along with the contents of the compounds in this CMP, ten representative compounds were chosen as the Q-markers for the quality control of SSOL. We find that five of these ten compounds, including danshensu, rosmarinic acid, salvianolic acid A, epimedin A and icariin, have antiarrhythmic activity. Then, the UPLC-DAD method was established as the control method for SSOL.

[Research on optimal modeling strategy for licorice extraction process based on near-infrared spectroscopy technology].

The manufacture of traditional Chinese medicine (TCM) products is always accompanied by processing complex raw materials and real-time monitoring of the manufacturing process. In this study, we investigated different modeling strategies for the extraction process of licorice. Near-infrared spectra associate with the extraction time was used to detemine the states of the extraction processes. Three modeling approaches, i.e., principal component analysis (PCA), partial least squares regression (PLSR) and parallel factor analysis-PLSR (PARAFAC-PLSR), were adopted for the prediction of the real-time status of the process. The overall results indicated that PCA, PLSR and PARAFAC-PLSR can effectively detect the errors in the extraction procedure and predict the process trajectories, which has important significance for the monitoring and controlling of the extraction processes.