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

Abstract The detection of microplastics in water 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 machine (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 the measured impedances agree with literature data. The subsequent classification of measured impedances by SVM shows that the three classes “no plastic” (below the detection limit of 1 g plastic per filling), “PP” and “PO” can be distinguished securely independent of the background medium water. Mixtures of PO and PP were not examined, i.e. either PO or PP was filled into the measuring cell. An SVM regression performed after the SVM classification yields the microplastic concentration of the respective sample. Further tests with varying salinity and content of organic or biological material in the water confirmed the good results. We conclude that EIS in combination with machine learning (MLEIS) seems to be a promising approach for in situ detection of microplastics and certainly warrants further research activities.