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. Verschegina - 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

Thiabendazole (TBZ) detection by SERS is complicated by overlapping signals from the solvent and nanostructured substrate. Here, two approaches for analysing concentration-dependent TBZ spectra were compared across four independent measurement series: direct measurement of the characteristic band at 1592 cm⁻¹ and reference-guided two-component spectral modelling. Singlepeak analysis was simple but showed considerable between-series variability, whereas the full-spectrum model supported TBZ identification and provided a lower preliminary LOD in three of four series. The best estimated LOD was 10.2 nM. The methods were evaluated using static measurements and will next be validated using fabricated flow-through microfluidic SERS sensor prototypes.