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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Chem. | doi: 10.3389/fchem.2018.00388

Accuracy improvement of in-line near infrared spectroscopic moisture monitoring in a fluidized bed drying process

 Andrey Bogomolov1, 2, 3*, Joachim Mannhardt4 and Oliver Heinzerling5
  • 1Blue Ocean Nova AG, Germany
  • 2Samara State Technical University, Russia
  • 3Global Modelling, Germany
  • 4Blue Ocean Nova Gmbh, Germany
  • 5AbbVie (Germany), Germany

Exploratory analysis of a large representative dataset obtained in a fluidized bed drying of a pharmaceutical powder has revealed a significant correlation of the spectral intensity with the granulate humidity in the whole studied range of 1091.8–2106.5 nm. The effect was explained by dependence of the powder refractive properties, and hence light penetration depth, on the water content. This effect exhibited close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of the prediction accuracy, compared to the scatter-corrected data. Further improvement of the model performance was achieved by application of a dynamic focusing strategy when adjusting the model to a drying process stage. The probe fouling was shown to have a minor effect on the prediction accuracy. The study resulted in a considerable reduction of the root mean-square error of in-line moisture monitoring to 0.1%, which is close the reference method reproducibility and significantly better than previously reported results.

Keywords: Fluidized bed drying, Moisture monitoring, NIR spectroscopy, Scatter correction, Lighthouse Probe, Process Analytical Technology

Received: 16 Apr 2018; Accepted: 10 Aug 2018.

Edited by:

Federico Marini, Università degli Studi di Roma La Sapienza, Italy

Reviewed by:

Huawen Wu, BaySpec, Inc., United States
Ludovic Duponchel, Université de Lille, France  

Copyright: © 2018 Bogomolov, Mannhardt and Heinzerling. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Andrey Bogomolov, Blue Ocean Nova AG, Aalen, 73430, Germany,