Near-infrared (NIR) spectroscopy has become an important process analytical technology (PAT) for monitoring and implementing control in continuous manufacturing (CM) schemes. However, NIR requires complex multivariate models to properly extract the relevant information and the traditional model of choice, partial least squares, can be unfavorable on account of its high material and time investments for generating calibrations. To account for this, pure component-based approaches have been gaining attention due to their higher flexibility and ease of development. In the present study, the application of two pure component approaches, classical least squares (CLS) models and iterative optimization technology (IOT) algorithms, to pharmaceutical powder blends in a continuous feed frame was considered. The approaches were compared from both a model performance and practical implementation perspective. IOT were found to demonstrate superior performance in predicting drug content compared to CLS. The practical implementation of each modelling approach was also given consideration.

• Duquesne University
• Pfizer, Inc.

• PAT
• Modeling

1. Classical least squares (CLS)
2. continuous manufacturing
3. Iterative optimization technology (IOT)
4. modeling
5. Near-infrared spectroscopy
6. pat
7. Process analytical technology (PAT)
8. Research Article