Hyperspectral imaging for identification of irregular-shaped microplastics in water.

In this article, we demonstrate detection and identification of ten microplastic types directly in a water sample using an identification table derived from microplastic hyperspectral images. We selected a total of fourteen wavelengths which can be used to distinguish these ten microplastic types. We enhanced the visibility of these wavelengths by computationally removing water and baseline correcting with reflectance at 1550 nm. This method avoids, prevents, and eases most of the laborious sample preparation mandatory prior to analysis with robust techniques such as Raman spectroscopy and Fourier transform infrared spectroscopy. The ten different plastics were studied in water, first separately and then in a mixture. The microplastic concentrations varied depending on microplastic type and were kept <12 mg/ml per type. Finally, detection and identification were confirmed pixel-wise in a hyperspectral image of a realistic water matrix simulant including mixtures of only a few microplastic particles. All measurements have been performed with microplastics of different sizes and irregular shapes made in-house by milling commercial pellets and sheets. It enabled the establishment of a procedure for the identification of these vicious particles in real water samples. The present measurement setup of hyperspectral imaging and method of data analysis of a mixture of microplastics directly from a water-based sample may open a path towards fast, reliable, and on-site detection.