Automatic classification and permittivity estimation of organic solvents using a dielectric resonator sensor and machine learning techniques

M. Monteagudo Honrubia, F.J. Herraiz-Martínez, J. Matanza

XXXVII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2022, Málaga (España). 05-07 septiembre 2022

Resumen:

This paper presents the application of a dielectric resonator sensor to characterize organic solvents. Two different acquisition systems were implemented to test the sensor and compare the results between a Vector Network Analyzer (VNA) and a low-cost portable electronic reader presented in this paper. Five dissolutions and air were measured within a permittivity range from 1 to 80. Principal Component Analysis (PCA) and Support Vector Machine (SVM) were used to perform automatic classification achieving an accuracy close to the 100% for both systems.

Fecha de publicación: septiembre 2022.

Cita:
Monteagudo Honrubia, M., Herraiz-Martínez, F.J., Matanza, J., Automatic classification and permittivity estimation of organic solvents using a dielectric resonator sensor and machine learning techniques, XXXVII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2022, Málaga (España). 05-07 septiembre 2022.

IIT-22-112C