Rapid Judgement of Blending Endpoint of Jingqi Shuangshen Capsules and Content Determination of Astragaloside Ⅳ by AOTF-NIR / 中国药房

OBJECTIVE:To establish the method for rapid judgement of blending endpoint of Jingqi shuangshen capsules and content determination of astragaloside Ⅳ. METHODS:AOTF-NIR combined with principal component analysis and Moving Block Standard Deviation method was used to identify the blending endpoint. First derivative combined with savitzky-golay filter method were used to spectrum pretreatment. The partial least square method was used to establish quantitative analysis model of the content of astragaloside Ⅳin mixed endpoint sample. The content of astragaloside Ⅳ in mixed endpoint sample was determined by HPLC-ELSD to validate the model. RESULTS:Methodology validation of content determination of astragaloside Ⅳ in mixed material sample and mixed endpoint sample was in line with the requirements. NIR monitoring results showed that the product reached the blending endpoint after 30 min. The results of NIR monitoring were generally consistent with the results of HPLC-ELSD. The principal component dimension of the quantitative model was 9;determination coefficients was 0.954 9;Root Mean Square of Calibration of the model was 0.039 2;Root Mean Square Error of Prediction of the model was 0.042 6. Predicted average value of astragaloside Ⅳ by NIR was 11.74 mg/g,and measured average value of astragaloside Ⅳ by HPLC-ELSD was 11.38 mg/g;average deviation was 3.16%. CONCLUSIONS:AOTF-NIR can rapidly judge the blending endpoint sample of Jingqi shuangshen capsules,rapidly determine the content of astragalosideⅣin mixed endpoint material,improve the quality control level of blending process and shorten blending cycle.

Online endpoint detection algorithm for blending process of Chinese materia medica / 中国中药杂志

Blending process, which is an essential part of the pharmaceutical preparation, has a direct influence on the homogeneity and stability of solid dosage forms. With the official release of Guidance for Industry PAT, online process analysis techniques have been more and more reported in the applications in blending process, but the research on endpoint detection algorithm is still in the initial stage. By progressively increasing the window size of moving block standard deviation (MBSD), a novel endpoint detection algorithm was proposed to extend the plain MBSD from off-line scenario to online scenario and used to determine the endpoint in the blending process of Chinese medicine dispensing granules. By online learning of window size tuning, the status changes of the materials in blending process were reflected in the calculation of standard deviation in a real-time manner. The proposed method was separately tested in the blending processes of dextrin and three other extracts of traditional Chinese medicine. All of the results have shown that as compared with traditional MBSD method, the window size changes according to the proposed MBSD method (progressively increasing the window size) could more clearly reflect the status changes of the materials in blending process, so it is suitable for online application.