摘要
目的:利用近红外光谱技术结合自适应主成分分析(Principal Components Analysis, PCA)算法实现批次差异较大碳酸钙D3颗粒混合终点判断。方法:利用滚动PCA模块空间变化计算混合过程中模块之间的马氏距离,反映碳酸钙D3颗粒混合过程变化趋势,并设定阈值指导混合终点。结果:通过自适应PCA空间结构进行降维并预测当前混合点位与中心点位的马氏距离,通过结合混合颗粒情况确定30个光谱作为1个块,并同时跟进2组马氏距离趋势,明确控制阈值为1.73,为验证方法可靠性,采用HPLC方法对维生素D3活性成分进行含量检测,实测均匀度结果与自适应PCA算法判定结果一致。结论:使用自适应PCA算法可用于碳酸钙D3颗粒混合过程监控并进行终点判断,本方法可随颗粒体系的变化灵活调整参数,从而快速调控混合终点,以期为颗粒中间体或终产品的混合混匀度监测提供新思路。
Objective: To utilize near-infrared spectroscopy technology combined with the adaptive principal components analysis (PCA) algorithm to determine the mixing endpoint of calcium carbonate D3 granules with significant batch differences. Methods: The space variation of the rolling PCA module was used to calculate the Mahalanobis distance between modules during the mixing process, reflecting the changing trend of the mixing process of calcium carbonate D3 granules, and a threshold was set to guide the mixing endpoint. Results: The dimensionality reduction was carried out through the adaptive PCA spatial structure to predict the Mahalanobis distance between the current mixing point and the center point. By combining the conditions of mixing particles, 30 spectra were selected as one block, and the two sets of Mahalanobis distance trends were simultaneously followed up. The control threshold was clearly set at 1.73. To verify the reliability of the method, the content of vitamin D3 was detected by HPLC. The measured uniformity results showed consistency with the determination results of the adaptive PCA algorithm. Conclusion: The adaptive PCA algorithm can be used to monitor the mixing process of calcium carbonate D3 granules and determine the mixing endpoint. This method can flexibly adjust parameters according to the changes of the particle system to quickly control the mixing endpoint, with the aim of providing a new idea for the monitoring of the mixing uniformity of particle intermediates or final products.
出处
《药物化学》
2025年第4期305-314,共10页
Hans Journal of Medicinal Chemistry
