Pixel-based Raman hyperspectral identi fi cation of complex pharmaceutical formulations

Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations. (c) 2021 Published by Elsevier B.V.

Automated summary

The study introduces a pixel-based identification (PBI) approach for identifying spectral signatures in Raman hyperspectral imaging data. By reducing the number of spectral signatures through the identification of essential spectral pixels (ESP), direct database matching can be performed efficiently and rapidly, enabling the analysis of complex pharmaceutical formulations.