Hierarchical Multivariate Curve Resolution Coupled to Raman Imaging for Fast Characterization of Pharmaceutical Tablets

Purpose Although the characterization of the chemical and spatial distribution of compounds within a pharmaceutical tablet is still not a routine task, applying Raman spectroscopy with data analysis methods gives the possibility to obtain in-depth information on tablet quality rapidly. However, constraints such as analysis time, laser intensity, and spot size influence the quality of acquired spectra resulting in low signal-to-noise ratio spectra. Therefore, this study proposes a method to characterize a solid heterogeneous pharmaceutical product (e.g., a tablet) based on the product’s Raman chemical map. Methods In this work, surface Raman data were acquired using a simple and rapid method. An algorithm based on the hierarchical application of multivariate curve resolution with log-likelihood maximization combined with principal component analysis was used to blind identify the compounds and create a chemical map. Results Although the direct application of multivariate curve resolution algorithms did not allow a complete tablet characterization, the hierarchical application enabled individual compounds acquired from the mixed spectra to be identified and their chemical distribution in the tablet to be mapped without the use of external references. Results were successfully benchmarked against the EDXS analysis. Conclusions Innovations in multivariate methods could help overcome challenges and constraints in data acquisition. This method was, for example, found to be more robust against the presence of spectral outliers. It is promising for 3D analysis of real and complex samples. Graphical abstract