Artificial Intelligence-Assisted Raman Spectroscopy in Pharmacology and Pharmaceutical Manufacturing

Abstract

Raman spectroscopy integrated with artificial intelligence (AI) provides a non-destructive method for pharmaceutical quality control and process monitoring. This study evaluates a Raman–AI workflow designed for compound identification, spectral classification, and the detection of counterfeit pharmaceuticals. Spectra were collected from representative formulations, including paracetamol, ibuprofen, aspirin, and amoxicillin. The data underwent automated preprocessing and dimensionality reduction before being analyzed using Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) models. The CNN model achieved a classification accuracy of 99.1%, while the SVM provided comparable performance with lower computational requirements. The framework also demonstrated high sensitivity and specificity in distinguishing authentic from counterfeit formulations. These results indicate that combining Raman spectroscopy with machine learning models offers a reliable approach for automated analysis and process monitoring in pharmaceutical manufacturing.