在流体化床干燥过程中对颗粒的水分含量进行定量分析,同时使用近红外和拉曼光谱学,并与多变量模型相结合
1Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, PR China.
International journal of pharmaceutics
|January 8, 2026
概括
与拉曼光谱相比,近红外 (NIR) 光谱与部分最小平方 (PLS) 模型为在流化床干燥 (FBD) 期间监测颗粒含水量提供了更高的准确性,从而可以精确地确定终点.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 过程分析技术 (PAT) 是一种分析技术.
- 频谱学是一种光谱学.
背景情况:
- 有效监测颗粒含水量 (MC) 对于液化床干燥 (FBD) 的质量控制至关重要.
- 精确确定干燥终点可确保制药制造中的产品质量和工艺效率.
- 传统方法可能耗时,需要先进的技术进行实时分析.
研究的目的:
- 将近红外 (NIR) 和拉曼光谱进行比较,用于在基酸盐 (HCQ) FBD期间监测颗粒MC.
- 评估化学测量模型 (PLS,SVM) 和用于MC测定的光谱预处理技术的性能.
- 为实时过程理解和干燥端点检测建立一个综合方法.
主要方法:
- 在大型HCQ制造过程中获得的NIR和拉曼光谱数据的比较分析.
- 使用部分最小平方 (PLS) 和支持向量机 (SVM) 回归的校准模型的开发.
- 应用各种光谱预处理技术 (SNV,MSC,SG,ND,衍生品,OSC) 来提高模型的准确性.
主要成果:
- 与PLS相结合的NIR光谱学证明了MC监测的优异预测性能 (R2p=0.9990,RMSEP=0.105).
- 使用SVM的拉曼光谱显示出良好的性能 (R2p=0.9600,RMSEP=0.474),但精度低于NIR.
- 在线拉曼光谱为NIR结果提供了外部验证,提高了对MC分析的信心.
结论:
- 在线NIR光谱,与湿度/温度数据记录器集成,提供FBD的实时过程理解.
- 这种综合方法可以准确快速地确定干燥终点,通常在1.5%至2.5%MC之间.
- 该研究验证了先进的光谱技术的使用,以在制药干燥过程中进行强有力的质量控制.
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