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oai:figshare.com:article/34018429 2026-09-28T20:15:24Z category_146 category_7 category_19 category_21 category_272 category_873 category_734 category_931 category_64 category_135 portal_63 item_type_6 month_year_09_2026

SERS Combined with PCA-SVM-Based Identification Study of Different Brands of Levetiracetam Hong Zhang (25820) Nan Li (155066) Wuliji Hasi (3604067) Wenjuan Liu (467497) Space Science Medicine Pharmacology Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Computational Biology verify api content unique spectral fingerprints switching antiepileptic drugs specific differences  site pharmaceutical screening preventing counterfeit drugs preparation process  model achieved 100 fold stratified cross ensuring formulation stability ensure therapeutic consistency enhanced raman spectroscopy drug quality control characteristic raman peaks bioequivalence risks associated batch consistency evaluation based identification study precise brand identification demonstrating robust brand term epilepsy therapy support vector machine validating lev ’ patients undergoing long brand authentication term treatment machine learning thus provides silver sol rhodamine 6g reliable strategy potassium iodide optimized probe market surveillance integrated theoretical independent tool experimental approaches discrimination capability different manufacturers different brands crystal form cannot distinguish aggregating agent aeds ). 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. 2026-09-28T00:00:00Z Text Journal contribution 10.1021/acsomega.5c12399.s001 https://figshare.com/articles/journal_contribution/SERS_Combined_with_PCA-SVM-Based_Identification_Study_of_Different_Brands_of_Levetiracetam/34018429 CC BY-NC 4.0