Research on Quality Markers of Moutan Cortex: Quality Evaluation and Quality Standards of Moutan Cortex

Objective To identify the quality markers of Moutan Cortex（MC） and establish the quality evaluation methods for multi-component assay and fingerprinting of MC. Methods The chemical constituents in MC were identified by HPLC-QTOF-MS. UPLC was employed for the multi-component assay and fingerprinting of MC. Furthermore, text mining was carried out to review the biosynthesis pathways and pharmacological and pharmacokinetic studies related to MC, and in silico target fishing was conducted to construct compound-target networks for MC. Results Sixteen compounds were clearly identified in MC and their structures were confirmed through comparison with literature data. In addition, the biosynthetic pathways and component specificities of the identified compounds were summarized and confirmed by text mining.Pharmacological activities, including traditional usage and modern pharmacological studies were summarized. A total of 282 targets from Homo sapiens were fished for 13 compounds. In addition, pharmacokinetic studies of different compounds were synopsized. Finally, multi-component assay and fingerprint of MC were established. Conclusion Eight major components are selected as quality markers of MC, such as oxypaeoniflorin, apiopaeonoside, albiflorin, paeonolide, paeoniflorin, 1,2,3,4,6-penta-O-galloyl-β-D-glucose, mudanpioside C and paeonol. These eight quality markers are successfully applied to the quality evaluation of MC, and could be useful in improving the current quality standards of MC.

Application research of multivariate linkage fluctuation analysis on condition evaluation in process industry

Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.