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, Malaga (Spain). 05-07 September 2022

Summary:

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.

Published in URSI 2022, pp: 1-4, ISBN: 978-84-09-44537-0

Publication date: 2022-12-31.

Citation:
M. Monteagudo Honrubia, F.J. Herraiz-Martínez, J. Matanza, 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, Malaga (Spain). 05-07 September 2022. In: URSI 2022: Libro de actas del XXXVII Simposio Nacional de la Unión Científica de Radio, Málaga, 5 a 7 de septiembre de 2022, ISBN: 978-84-09-44537-0