SERS Combined with PCA-SVM-Based Identification Study of Different Brands of Levetiracetam

Abstract

This study addresses critical concerns in epilepsy management regarding generic drug substitution and the bioequivalence risks associated with switching antiepileptic drugs (AEDs). For patients undergoing long-term treatment, precise brand identification of levetiracetam (LEV) holds significant clinical value to ensure therapeutic consistency and safety. To meet this need, we developed a qualitative brand discrimination method based on surface-enhanced Raman spectroscopy (SERS) combined with machine learning. Silver sol was used as the SERS substrate, with potassium iodide as an aggregating agent and Rhodamine 6G (R6G) as an optimized probe. After validating LEV’s characteristic Raman peaks through integrated theoretical and experimental approaches, spectra from three commercial LEV brands were analyzed using principal component analysis (PCA) and support vector machine (SVM) classification. The model achieved 100% accuracy, precision, and sensitivity in 10-fold stratified cross-validation, demonstrating robust brand-discrimination capability. Unlike conventional quantitative assays that verify API content but cannot distinguish between different manufacturers, this SERS-based approach detects formulation-specific differences─such as variations in crystal form and preparation process─through unique spectral fingerprints. It thus provides a rapid, label-independent tool for brand authentication, batch consistency evaluation, and on-site pharmaceutical screening, effectively complementing pharmacopeial methods. This work supports medication safety and offers a reliable strategy for drug quality control and market surveillance, particularly in preventing counterfeit drugs and ensuring formulation stability in long-term epilepsy therapy.