Detection of microplastics in water using electrical impedance spectroscopy and support vector machines

Abstract:
The detection of microplastics in water currently requires a series of processes (sample collection, purification, and preparation) until a sample can be analyzed in the laboratory. To shorten this process chain, we are investigating whether electrical impedance spectroscopy (EIS) enhanced by a classifier based on support vector machines (SVM) can be applied to the problem of microplastics detection. Results with suspensions of polypropylene (PP) and polyolefin (PO) in deionized water proved promising: The relative permittivities extracted from measured impedances agree with literature data. The subsequent classification of measured impedances by SVM shows that the three classes “no plastic”, “PP”, and “PO” can be distinguished securely and that the microplastics concentration can be estimated quantitatively. We conclude that machine-learning-enhanced EIS (MLEIS) appears to be a promising approach for in-situ microplastics detection and certainly warrants more research activities.