ABSTRACT
The quality evaluation of traditional Chinese medicine is one of the key issues related to the modernization of traditional Chinese medicine. The quality evaluation technology system of traditional Chinese medicine mainly includes traditional evaluation (traits, microscopic and physicochemical identification), chemical evaluation and biological evaluation. Due to the complex composition of traditional Chinese medicine, the single detection method in the above evaluation technology system usually cannot obtain sufficient quality information. The multi-source information fusion strategy can organically integrate data from multiple analysis and detection technologies to obtain more comprehensive information of samples and improve the quality evaluation effect. At present, multi-source information fusion strategy has been widely used in the fields of military, industrial and food, and it is still in its infancy in the field of quality evaluation of traditional Chinese medicine. This research introduces the definition, structure, method (algorithm) and fusion level of multi-source information fusion, summarizes its research progress in the origin traceability, variety identification and pharmaceutical analysis of traditional Chinese medicine, and sorts out the specific methods of data fusion in each literature. Finally, we summarized, prospected and discussed the application, development and existing problems of information fusion technology and its application in the quality evaluation of traditional Chinese medicine, in order to provide reference for broadening the application of this technology in the field of traditional Chinese medicine.
ABSTRACT
Chinese materia medica (CMM) is a complex system, which has characteristics of multiple components, diversity efficacy, and unpredictable relation among components, etc. Thus, constructing the quality evaluation and quality control model based on multi-disciplinary and multi-source information fusion is an innovative research idea for quality standards of CMM. The quality control system should been fully established to cover quality integrity and efficacy specificity of CMM based on the core concept of quality marker. Traditional quality assessment methods based on characteristic feature, chemical analysis including multiple chemical components and elementary substances, and biological evaluation were integrated by data fusion methods in this system. The correlation of the organic and elemental components with the specific effects should be established in order to verify the material basis. Finally, the Q-markers (including potential elemental Q-markers) with unique characteristics relevant to specific effect were analyzed and defined. The integrated quality indexes of Q-markers relevant to specific effect were calculated. This proposed integrated quality control system based on multi-source information fusion methods would enrich and perfect the quality evaluation of CMM.
ABSTRACT
Objective: The potency of multi-source information fusion technology was explored to improve the model calibration and prediction performance of Chinese medicine extraction process. Methods: The ethanol extraction process for isolating fat-soluble components from Salvia miltiorrhiza was taken as the research carrier. S. miltiorrhiza from different sources were collected to simulate the fluctuation of materials. The changes of process parameters were simulated by design of experimental (DOE), and the process near infrared spectra (NIRS) were used as the process state variables. The contents of tanshinone IIA, cryptotanshinone, and tanshinone I were determined by HPLC. The raw material properties, process parameters and process state variables were combined as independent variables. The content of effective components in the extract was taken as the dependent variable. The partial least squares (PLS) algorithm was used to establish the quality prediction model of the extracts. Results: The modeling results respectively showed that the RMSECV was 0.172 8 mg/g, RMSEP was 0.031 7 mg/g, RPD was 6.91 (tanshinone IIA); RMSECV was 0.153 4 mg/g, RMSEP was 0.024 2 mg/g, RPD was 4.02 (cryptotanshinone); RMSECV was 0.117 1 mg/g, RMSEP was 0.043 2 mg/g, RPD was 4.76 (tanshinone I). Conclusion: The calibration and prediction performance of multi-source information fusion model are better than the conventional model, which can effectively improve the quality predictability and controllability of S. miltiorrhiza extract.