Approaches to Detect Microplastics in Water Using Electrical Impedance Measurements and Support Vector Machines

Abstract

We propose electrical impedance spectroscopy (EIS) enhanced by machine learning (ML), in particular support vector machines (SVMs), as a noninvasive, in situ method for detecting microplastics in water and as an alternative to slow, expensive laboratory measurements. The primary measurand is the complex-valued electrical impedance of a water-filled measuring cell. We carried out stationary measurements on numerous water samples contaminated with different plastic concentrations in a cylindrical measuring cell and in the frequency range from 20 Hz to 2 MHz. The effects of various influence quantities, such as the concentration of organic material (1.0%, 3.0%, 5.0%) or the salinity of water (0.5%, 1.0%, 3.5%), were also investigated. Measurements at 2 MHz with water flows carrying microplastic particles of different materials and sizes served to investigate the dynamic capabilities of the measurement method. The impedance spectra (stationary measurements) or the measured impedances (dynamic measurements) were then evaluated by specific SVMs. The classification task consisted of distinguishing different plastic materials and particle sizes. In the stationary case, the application of the SVM resulted in assignment accuracies of over 98%. In the dynamic case, the classification accuracies exceeded 91% for the mere classification of particle sizes and 85% for the classification of plastic particles by size and material.