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Characterization of NIR interfaces for the feeding and in-line monitoring of a continuous granulation process

By Román-Ospino, Andrés D; Tamrakar, Ashutosh Igne, Benoît Dimaso, Elyse Towns Airiau, Christian Clancy, Donald J Pereira, Glinka Muzzio, Fernando J Singh, Ravendra; Ramachandran, Rohit

Published on

Abstract

This work describes the characterization of three NIR interfaces intended to monitor a continuous granulation process. Two interfaces (i.e. a barrel interface and a rotating paddle interface) were evaluated to monitor the API concentration at the entrance of the granulator, and a third interface (i.e. an outlet interface), was evaluated to examine the quality of the resulting outlet granules. The barrel interface provided an assessment of the API concentration during the feeding process by scanning the material conveyed by the screws of the loss-in-weight feeder. The rotating paddle interface analyzed discrete amounts of powder upon exiting the feeder via the accumulation of material on the paddles. Partial Least Squares (PLS) calibration models were developed using the same powder blends for the two inlet interfaces and using the outlet granules for the outlet interface. Five independent batches were used to evaluate the prediction performance of each inlet calibration model. The outlet interface produced the lowest error of prediction due to the homogeneity of the granules. The barrel interface produced lower errors of prediction than the rotating paddle interface. However, powder density affected only the barrel interface, producing deviations in the predicted values. Therefore, powder density is a factor that should be considered in the calibration sample design for spectroscopic measurements when using this type of interface. A variographic analysis demonstrated that the continuous 1-dimensional motion in the barrel and outlet interfaces produced representative measurements of each batch during calibration and test experiments, generating a low minimum practical error (MPE).

Journal

International Journal of Pharmaceutics. Volume 574, 2020, 118848

DOI

10.1016/j.ijpharm.2019.118848

Type of publication

Peer-reviewed journal

Affiliations

  • Rutgers, The State University of New Jersey

Article Classification

Research article

Classification Areas

  • Oral doses
  • Modeling
  • Process Control

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