Nieuwmeyer Florentine J S, Damen Michiel, Gerich Ad, Rusmini Federica, van der Voort Maarschalk Kees, Vromans Herman
Department of Pharmaceutics, NV Organon, P.O. Box 20, 5340 BH, Oss, The Netherlands.
Pharm Res. 2007 Oct;24(10):1854-61. doi: 10.1007/s11095-007-9305-5. Epub 2007 May 5.
Water content and granule size are recognized as critical process and product quality parameters during drying. The purpose of this study was to enlighten the granule behavior during fluid bed drying by monitoring the major events i.e. changes in water content and granule size.
NIR spectra collected during drying and water content of sampled granules were correlated by principal component analysis (PCA) and partial least squares regression (PLSR). NIR spectra of dried granules were correlated to median granule size in a second PCA and PLSR.
The NIR water model discriminates between various stages in fluid-bed drying. The water content can be continuously predicted with errors comparable to the reference method. The four PLS factors of the granule size model are related to primary particle size of lactose, median granule size exceeding primary particle size and amorphous content of granules. The small prediction errors enable size discrimination between fines and granules.
For product quality reasons, discrimination between drying stages and end-point monitoring is highly important. Together with the possibilities to determine median granule size and to distinguish fines this approach provides a tool to design an optimal drying process.
在干燥过程中，水分含量和颗粒大小被视为关键的工艺和产品质量参数。本研究的目的是通过监测主要事件，即水分含量和颗粒大小的变化，来揭示流化床干燥过程中的颗粒行为。
通过主成分分析（PCA）和偏最小二乘回归（PLSR）将干燥过程中收集的近红外光谱与取样颗粒的水分含量进行关联。在第二次PCA和PLSR中，将干燥颗粒的近红外光谱与颗粒中位径进行关联。
近红外水分模型能够区分流化床干燥的各个阶段。水分含量可以连续预测，预测误差与参考方法相当。颗粒大小模型的四个PLS因子与乳糖的初级粒径、超过初级粒径的颗粒中位径以及颗粒的无定形含量有关。较小的预测误差使得能够区分细粉和颗粒。
出于产品质量的原因，区分干燥阶段和终点监测非常重要。结合确定颗粒中位径和区分细粉的可能性，这种方法为设计优化的干燥工艺提供了一种工具。
