NIR Chemical Imaging Can Help Ensure Safety in Pharma Manufacturing

Researchers evaluated the ability of NIR chemical imaging (NIR CI) to monitor the concentration of pharmaceutical ingredients in tableting powder over a wide area. Results showed that NIR CI-based predictions of concentration could potentially be more representative than those achieved using NIR spectroscopy (NIRS), since the NIR CI technique gathers information from a comparatively larger sample area. Because of the relatively small area analyzed using NIRS, areas of segregation can be missed, leading to differences in concentrations of the active pharmaceutical ingredient in individual tablets.

The study monitored a prototype pharmaceutical composition containing ascorbic acid (AA), microcrystalline cellulose and dicalcium phosphate. In-line calibration models based on partial least square regression were developed and validated with a range of AA concentrations. The ability of NIRS and NIR CI to predict concentrations in test runs was ascertained both independently and in combination.

Process analytical technology tools enable critical process parameters and critical quality attributes measurements in-line, on-line and at-line during the manufacture of different dosage forms, such as tablets, capsules and liquids. NIR chemical imaging (NIR CI) can monitor the concentration of pharmaceutical ingredients in tableting powder over a wide area and help ensure quality. NIRS = NIR spectroscopy; PLS = partial least square. Courtesy of H. Dalvi, C. Fauteux-Lefebvre, J.-M. Guay, N. Abatzoglou and R. Gosselin.

NIR CI, with a single bandpass filter, predicted AA concentrations, present at commercially relevant concentrations, with acceptable accuracy. In the setup used, sample volume tested by NIR CI was five times higher than NIRS. Considering the possibility of adjusting the flat insert size, researchers believe there is potential to further increase NIR CI sample size to meet unit dose samples at feed frame.

In addition to the advantage of increased sample size, NIR CI has the potential to detect segregation inside feed frames. With NIR CI, different image sections can be analyzed separately in the event of localized concentration changes. An in-line feed frame monitoring system using NIR CI could help obtain quantitative and visual presentations of powder composition, which could be useful for real-time process monitoring.

The study indicates that NIR CI alone or in combination with NIRS appears to be a promising tool for in-line feed frame monitoring.

“Concentration predictions with NIR chemical imaging were found to be similar to those of the NIR spectroscopy model. However, NIR chemical imaging is better positioned to view concentration modifications over larger sample areas, and different image sections can be analyzed separately in the event of localized concentration changes,” said professor Ryan Gosselin from the Université de Sherbrooke.

The research was published in Journal of Spectral Imaging (doi:10.1255/jsi.2018.a5).