Studies On Quality Control Of The Productive Process Of Ganmaoling Based On NIRS

Ganmaoling granules is made up of four kinds of traditional Chinese medicines (TCM) and three kinds of chemical medicines and has become the first brand of domestic cold medicine. It is widely applied in clinical treatment of headache, fever, stuffy nose, runny nose and sore throat. However, the quality control of ganmaoling granules is given priority to finished product and intermediates and the quality control of production process is often ignored. For extraction, concentration, alcohol precipitation and other production process, it still relies heavily on the judgment of workers to control quality and there is no clear analysis to ensure the stability of the production process. So it is imperative to develop the quality of production process of ganmaoling and other kinds of TCM.With the rapid development of chemometrics, the NIRS analysis technology is widely used in pharmaceutical field. This study researched the process of extraction, alcohol precipitation and concentration of ganmaoling and established quantitative and qualitative analysis models to improve the quality control, which has important and realistic significance. The main research achievements are as follows:(1) NIRS was employed to establish online quantitative models of linarin, chlorogenic acid and solid content in extraction of ganmaoling. The results showed that the three indicators can be predicted quickly and accurately and displayed online. Besides, the Relative Standard Errors of Prediction (RSEP) were controlled within 10%, which is sufficient to the quantitative detection of extraction process.(2) NIRS combined with ehemometrics, was used to establish quantitative models for the process of alcohol precipitation and linarin, chlorogenic acid and the content of solid were determined as indicators. The PSO-LS-SVM analysis models were established. The results showed that the PSO-LS-SVM models had better prediction accuracy than the PLSR models, which suggested the PSO-LS-SVM algorithm has good application prospect.(3) In the process of concentration, qualitative and quantitative models were established, combined with multivariate statistical process control. the content of linarin, chlorogenic acid and density were detected by traditional methods to verify the PLSR models and the RSEP of three indicators were controlled within 10%. The results showed that the MSPC model can monitor the process and identify abnormal batch, qualitatively and steadily. And the PLSR models, built in the study, can predict the indicators quickly and effectively.