From laboratory- to pilot-scale: moisture monitoring in fluidized bed granulation by a novel microwave sensor using multivariate calibration approaches. (3rd June 2018)

• Record Type: Journal Article
• Authors: Peters, Johanna; Taute, Wolfgang; Döscher, Claas; Meier, Robin; Höft, Michael; Knöchel, Reinhard; Breitkreutz, Jörg
• Is Part Of: Drug development and industrial pharmacy. Volume 44:Number 6(2018)
• Page Start: 961
• Page End: 968
• Publication Date: 2018-06-03
• Subjects: Microwave resonance technology -- in-line moisture measurement -- process analytical technology -- multiple linear regression -- partial least squares regression -- principal component regression; Pharmaceutical chemistry -- Periodicals; Pharmaceutical industry -- Periodicals; Drug Industry -- Periodicals; Technology, Pharmaceutical -- Periodicals; 615.05
• DOI: 10.1080/03639045.2018.1425427
• Languages: English
• ISSNs: 0363-9045

Abstract

Abstract: Recently, microwave resonance technology (MRT) sensor systems operating at four resonances instead of a single resonance frequency were established as a process analytical technology (PAT) tool for moisture monitoring. The additional resonance frequencies extend the technologies' possible application range in pharmaceutical production processes remarkably towards higher moisture contents. In the present study, a novel multi-resonance MRT sensor was installed in a bottom-tangential-spray fluidized bed granulator in order to provide a proof-of-concept of the recently introduced technology in industrial pilot-scale equipment. The mounting position within the granulator was optimized to allow faster measurements and thereby even tighter process control. As the amount of data provided by using novel MRT sensor systems has increased manifold by the additional resonance frequencies and the accelerated measurement rate, it permitted to investigate the benefit of more sophisticated evaluation methods instead of the simple linear regression which is used in established single-resonance systems. Therefore, models for moisture prediction based on multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS) were built and assessed. Correlation was strong (all R 2 > 0.988) and predictive abilities were rather acceptable (all RMSE ≤0.5%) for all models over the whole granulation process up to 16% residual moisture. While PCR provided best predictive abilities, MLR proofed as a simple and valuable alternative without the need of chemometric data evaluation.