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

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.