Institute of Biotechnology and Bioengineering, IST, Technical University of Lisbon, Portugal.
Eur J Pharm Biopharm. 2011 Aug;78(3):513-21. doi: 10.1016/j.ejpb.2011.02.008. Epub 2011 Feb 17.
A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles.
采用微波共振技术（MRT）对 192 批工业规模的流化床制粒过程进行在线监测，以确定颗粒的水分、温度和密度。多元数据分析技术，如多向偏最小二乘（PLS）、多向主成分分析（PCA）和多元批处理控制图，应用于收集的批处理数据集。这些技术的结合，以及离线粒度测量，显著提高了对过程的理解。可以证明存在季节性效应，通过其对最终颗粒尺寸的影响来影响进一步的处理。此外，通过 PLS 证明了粒径与 MRT 测量之间存在定量关系，突出了 MRT 传感器预测最终颗粒尺寸信息的潜在能力。本研究有助于改进流化床制粒工艺，获得的过程知识表明，可以按照质量源于设计（QbD）和过程分析技术（PAT）原则在工艺设计中构建产品质量。
