NIR Spectral Analysis in Twin-Screw Melt Granulation: Effects of Binder Content, Screw Design, and Temperature

This study evaluates the feasibility of Near-Infrared (NIR) spectroscopy combined with chemometric modeling for monitoring twin-screw melt granulation. Lactose monohydrate was used as a model excipient and polyethylene glycol (PEG 6000) (Sistemas Analíticos S.A, Buenos Aires, Argentina) as a meltable binder. Granules were produced under different processing conditions by varying binder content, screw configuration (kneading or conveying elements), and measurement temperature. NIR spectra were acquired on-line on a conveyor belt and analyzed using Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression. The regression models showed excellent predictive performance for PEG 6000 content in lactose-based granules, with coefficients of determination higher than 0.998 for both raw and preprocessed spectral data. PCA successfully discriminated between granulated and non-granulated materials, as well as between granules produced with different screw configurations, demonstrating the sensitivity of the technique to processing conditions and granule formation mechanisms. In addition, spectral differences associated with measurement temperature were detected, with derivative-based preprocessing improving the discrimination between warm and cooled granules. Overall, the results demonstrate that NIR spectroscopy, coupled with multivariate analysis, is a robust and non-invasive tool for real-time monitoring of twin-screw melt granulation, with strong potential to enhance process understanding, control, and product consistency in continuous pharmaceutical manufacturing.