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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/223334
CSRR chemical sensing in uncontrolled environments by PLS regression
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Complementary 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.
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ALONSO VALDESUEIRO, Javier, FERNÁNDEZ ROMERO, Luis, GUTIÉRREZ GÁLVEZ, Agustín, MARCO COLÁS, Santiago. CSRR chemical sensing in uncontrolled environments by PLS regression. _IEEE Sensors Journal_. 2025. [consulta: 9 de gener de 2026]. ISSN: 1530-437X. [Disponible a: https://hdl.handle.net/2445/223334]