Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning

Abstract

Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to-use and rapid method for directly measuring and distinguishing MPs from other naturally present colloids in the environment would expedite analytical workflows. Here, we established a database of MP scattering and fluorescence properties, either alone or in mixtures with natural particles, by stain-free flow cytometry. The resulting highdimensional data were analyzed using machine learning approaches, either unsupervised (e.g., viSNE) or supervised (e.g., random forest algorithms). We assessed our approach in identifying and quantifying model MPs of diverse sizes, morphologies, and polymer compositions in various suspensions including phototrophic microorganisms, suspended biofilms, mineral particles, and sediment. We could precisely quantify MPs in microbial phototrophs and natural sediments with high organic carbon by both machine learning models (identification accuracies over 93%), although it was not possible to distinguish between different MP sizes or polymer compositions. By testing the resulting method in environmental samples through spiking MPs into freshwater samples, we further highlight the applicability of the method to be used as a rapid screening tool for MPs. Collectively, this workflow can be easily applied to a diverse set of samples to assess the presence of MPs in a time-efficient manner.