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

Abstract

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.

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.