# name
CSRR chemical sensing in uncontrolled environments by PLS regression

# metadata.dc.description.abstract[].value
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.

# metadata.dc.publisher[].value
Institute of Electrical and Electronics Engineers (IEEE)

# metadata.dc.rights[].value
cc-by (c) Alonso-Valdesueiro, Javier, et al., 2025

# metadata.dc.source[].value
Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)

# metadata.dc.title[].value
CSRR chemical sensing in uncontrolled environments by PLS regression