UV reflectance imaging-based component mapping of spironolactone tablets using a UV-sensitive camera compared with NIR and Raman chemical imaging

Szabó-Szőcs, Bence and Mészáros, Lilla Alexandra and Farkas, Attila and Pataki, Hajnalka and Honti, Barbara and Drávavölgyi, Gábor and Gergely, Szilveszter and Madarász, Lajos and Galata, Dorián László and Nagy, Zsombor Kristóf (2026) UV reflectance imaging-based component mapping of spironolactone tablets using a UV-sensitive camera compared with NIR and Raman chemical imaging. INTERNATIONAL JOURNAL OF PHARMACEUTICS, 702. No.-127201. ISSN 0378-5173 (print); 1873-3476 (online)

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Abstract

UV reflectance imaging-based machine vision can provide a fast alternative to established spectroscopic imaging methods for mapping the surface distribution of tablet components. In this study, the applicability of this approach was assessed by comparison with reference techniques using visually white model tablets containing spironolactone as a low-dose active pharmaceutical ingredient (2 w/w%), together with microcrystalline cellulose and lactose monohydrate as the main excipients. Five tablet batches containing different spironolactone particle size fractions were prepared. Selected samples representing the smallest, intermediate, and largest API particle size fractions were analyzed by UV reflectance imaging, NIR chemical imaging, and Raman chemical imaging for direct method comparison. UV reflectance imaging provided sufficient grayscale contrast to distinguish spironolactone, lactose monohydrate, and microcrystalline cellulose on the tablet surface, and the resulting maps showed qualitative agreement with the NIR and Raman distributions. In addition, UV-based fullsurface component maps were successfully obtained for all five particle size fractions. UV reflectance imaging required only a single short exposure for full-surface acquisition, whereas NIR and especially Raman mapping required substantially longer measurement times. These findings indicate the potential of UV reflectance imaging-based mapping as a rapid screening approach, yielding surface-distribution patterns that show reasonable agreement with established reference imaging techniques.

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