Criticalquality attribute assessment of big brand traditional Chinese medicine: visualization method for quality control of Ginkgo Leaves Tablets based on spatial distribution uniformity.
10.19540/j.cnki.cjcmm.20210218.304
- Author:
Ling LIN
1
;
Fang-Yu ZHANG
1
;
Jing ZHANG
1
;
Chao-Fu MA
1
;
Wen-Zhe WANG
1
;
Le-Ting LEI
1
;
Jin-Yuan ZHU
1
;
Zhong-Qing YAO
2
;
Min LI
2
;
Zhi-Sheng WU
1
Author Information
1. School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 102488, China Engineering Research Center of Chinese Medicine Production and New Drug Development, Ministry of Education Beijing 102488, China.
2. Traditional Chinese Medicine Manufacturing Technology National Engineering Research Center, Yangtze River Pharmaceutics Group Co., Ltd. Taizhou 225321, China.
- Publication Type:Journal Article
- Keywords:
Ginkgo Leaves Tablets;
big brand of traditional Chinese medicine;
critical quality attribute;
hyperspectral imaging;
spatial distribution uniformity
- MeSH:
Calibration;
Ginkgo biloba;
Least-Squares Analysis;
Medicine, Chinese Traditional;
Plant Leaves;
Quality Control;
Spectroscopy, Near-Infrared;
Tablets
- From:
China Journal of Chinese Materia Medica
2021;46(7):1616-1621
- CountryChina
- Language:Chinese
-
Abstract:
Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.
