Alonso Valdesueiro, JavierFernández Romero, LuisGutiérrez Gálvez, AgustínMarco Colás, Santiago2025-09-222025-09-222025-09-181530-437Xhttps://hdl.handle.net/2445/223334Complementary Split Ring Resonators (CSRRs) have been widely researched as planar sensors, but their use in routine chemical analysis is limited due to dependence on high-end equipment, controlled conditions, and susceptibility to environmental and handling variations. This work introduces a novel approach combining a CSRR sensor with machine learning (ML) to enable reliable quantification of compounds. A low-cost benchtop CSRR system was tested for ethanol concentration prediction in water (10–96%), using 450 randomized measurements. PCA was applied for data exploration, and a PLS regression model with Leave-One-Group-Out cross-validation achieved a 3.7% RMSEP, six times better than univariate calibration (23.4%). The results show that ML can mitigate measurement uncertainties, making CSRR sensors viable for robust, low-cost concentration analysis under realistic laboratory conditions.10 p.application/pdfengcc-by (c) Alonso-Valdesueiro, Javier, et al., 2025http://creativecommons.org/licenses/by/3.0/es/TermometriaRessonadorsAprenentatge automàticTemperature measurementsResonatorsMachine learninginfo:eu-repo/semantics/article7606252025-09-22info:eu-repo/semantics/openAccess