中药大品种制造关键质量属性表征:空间分布均匀度属性的银杏叶片质量控制可视化方法研究

Criticalquality attribute assessment of big brand traditional Chinese medicine:visualization method for quality control of Ginkgo Leaves Tablets based on spatial distribution uniformity

空间分布均匀度是中药大品种银杏叶片的关键质量属性(critical quality attribute,CQA)。银杏叶片中活性药物成分(active pharmaceutical ingredient,API)的空间分布均匀度评价是保证产品稳定可控的重要内容。该研究采用高光谱成像技术,基于3种预测模型构建API浓度空间分布图,实现银杏叶片空间分布均匀度的可视化研究。在每一银杏叶片中,选取感兴趣区域(region of interest,ROI),长宽均为50像素,共计2500个像素点,每个像素点有288个光谱通道,单个样品的含量预测数据量可达1×10^(5)。3种模型的预测结果表明,偏最小二乘(partial least squares,PLS)模型的预测准确性最高,校正集决定系数R^(2)_(pre)为0.987,预测集决定系数R^(2)_(pre)为0.942,校正均方根误差(root mean square error of calibration,RMSEC)为0.160%,预测均方根误差(root mean square error of prediction,RMSEP)为0.588%;经典最小二乘(classical least squares,CLS)模型的预测误差较大,RMSEP为0.867%;多元曲线校正-交替最小二乘(multivariate curve resolution-alternating least square,MCR-ALS)模型的预测能力三者中最差,其无法实现含量预测。基于PLS和CLS模型的预测结果,通过三维数据重构获得API浓度的空间分布图。进一步,采用直方图法,实现API的空间分布均匀度评价,数据表明银杏叶片中API的空间分布较为均匀。该研究基于3种模型探讨银杏叶片空间分布可视化的可行性。结果表明,PLS模型的预测准确性最高,MCR-ALS模型的预测准确性最低,研究结果为银杏叶片质量控制可视化方法提供新策略。

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 2500 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^(2)_(pre) of 0.987,prediction set determination coefficient R^(2)_(pre) 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.