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Microplastic Discrimination with Hyperspectral Microscopy
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Researchers explored hyperspectral microscopy -- which combines spatial imaging with point spectroscopy -- to discriminate microplastic types and assess contamination in environmental samples. The approach demonstrated enhanced material discrimination compared to conventional microscopy by leveraging both spatial and spectral information simultaneously.
Hyperspectral imaging combines the characteristics of computer vision and point spectroscopy by obtaining an image with both spatial and spectral information. Therefore, in combination with microscopy, it can increase material discrimination possibilities with respect to regular microscopy imaging. We explore the potential to assess microplastic contamination and discriminate the plastic material. For this purpose, a hyperspectral short wavelength-infrared (SWIR) camera is used in combination with reflection microscopy for pellet material identification. This camera provides high spectral and spatial resolution in the 1100–1650 nm range, 100 spectral bands and up to 640×512 spatial resolution and high acquisition speed. The analysis performed shows potential to accurately discriminate 22 tested plastic pellets. In addition, the band relevance analysis performed shows that only a few specific bands are needed to provide accurate discrimination of the tested materials. The hyperspectral method presented could lead to a faster contamination assessment than traditional techniques used for microplastic discrimination.
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