Tu-SML-01 - Recovery of Characteristic Thiabendazole Raman Peaks from Ethanol-Dominated Spectra Toward Microfluidic SERS-Based Pesticide Sensing
- Event
- EUROSENSORS 2026
2026-09-06 - 2026-09-09
Zurich - Band
- Poster
- Chapter
- Signal Analysis, Machine Learning, Physics-Informed Machine Learning And Artificial Intelligence For Sensing
- Author(s)
- K. Mileńko, K. A. H. Bakke, E. Vereshchagina - SINTEF Digital,Oslo (Norway), A. Baracu, A. Dinescu - National Institute for R&D in Microtechnologies - IMT Bucharest,Bucharest (Romania), O. Rasoga - National Institute of Materials Physics,Magurele (Romania), K. Giżyński - Polish Academy of Sciences,Warsaw (Poland)
- Pages
- 696 - 697
- DOI
- 10.5162/eurosensors2026/Tu-SML-01
- ISBN
- 978-3-910600-12-6
- Price
- free
Abstract
We present a reference-guided Raman analysis workflow developed as a signal processing module for a microfluidic SERS sensor targeting pesticide detection. A two-component linear model based on ethanol and dry thiabendazole (TBZ) reference spectra was used to recover analyte contributions from ethanol-dominated spectral data. The recovered TBZ component preserved key diagnostic peaks and enabled automated peak identification. The current results are based on measurements of a dried sample of 2 μL and serve as a proof-of-concept for future flow-through sensing systems.