From laboratory- to pilot-scale: moisture monitoring in fluidized bed granulation by a novel microwave sensor using multivariate calibration approaches.

From laboratory- to pilot-scale: moisture monitoring in fluidized bed granulation by a novel microwave sensor using multivariate calibration approaches.

Drug Dev Ind Pharm. 2018 Jan 06;:1-18

Authors: Peters J, Taute W, Döscher C, Meier R, Höft M, Knöchel R, Breitkreutz J

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 R2 > 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 principal component regression provided best predictive abilities, multiple linear regression proofed as a simple and valuable alternative without the need of chemometric data evaluation.

PMID: 29308682 [PubMed - as supplied by publisher]

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