Ultrasensitive SERS Detection and AI-Assisted Differentiation of Structurally Similar Pollutants on Flexible Aluminum Substrates
M. To address spectral complexity, a data-driven approach combining principal component analysis (PCA) and support vector machine (SVM) classification is employed, achieving a high classification accuracy of 96.7% for single analytes as well as binary and ternary mixtures across multiple concentration levels. The proposed approach offers strong potential for multiplex detection and practical applications in environmental monitoring, food safety, and on-site analytical sensing.
Authors
- C. Selvakumar
- Jayasree Kumar (ORCID: https://orcid.org/0000-0002-4566-1457)
- Rajapandiyan Panneerselvam (ORCID: https://orcid.org/0000-0002-3566-4558)
- Hemanth Noothalapati (ORCID: https://orcid.org/0000-0002-8279-7716)
- Wei‐Lung Tseng (ORCID: https://orcid.org/0000-0001-9808-5863)
- Shaik Imamvali (ORCID: https://orcid.org/0000-0003-2945-8747)
- Santhoshkumar S
Institutions
- Raman Research Institute (IN)
- National Sun Yat-sen University (TW)
- Shimane University (JP)
- Centre for Interdisciplinary Research and Education (IN)
- Nanomaterials Research (United States) (US)
- Dr. Reddy's Laboratories (India) (IN)
- SRM University (IN)
- Indian Institute of Technology Hyderabad (IN)
Publication Details
- Journal
- Analytical Chemistry
- Published
- 2026-07-26
- DOI
- https://doi.org/10.1021/acs.analchem.6c01951
- Primary Topic
- Gold and Silver Nanoparticles Synthesis and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00