Raman spectroscopy and multivariate analysis for identification and classification of pharmaceutical pain reliever tablets

The utilization of tablets for drug administration is one of the most advantageous and widely used methods of drug delivery. Because of its popular use and incredible importance, understanding, optimizing, and verifying the formulation of tablets are a crucial task. Herein, Raman spectroscopy in combination with chemometrics was used to accomplish two important tasks. First, the identification of the active pharmaceutical ingredient in four different pain reliever tablets was confirmed. Second, partial least squares discriminant analysis (PLS-DA) was applied to successfully classify Raman spectral data for accurate identification of each pain reliever tablet type. The developed methodology was externally validated by two different approaches, with results indicating the PLS-DA model could classify Raman spectral data from each of the four pain reliever types with 94.9%-100% sensitivity and specificity levels. The totality of this work can be used for deep understanding of pharmaceutical tablet identification, formulation, and manufacturing processes, thereby impacting the overall production of safe and effective tablets for drug delivery efforts.

Automated summary

The utilization of Raman spectroscopy with chemometrics is crucial for accurately identifying and classifying active ingredients in different tablets, impacting the safety and effectiveness of drug delivery.