An aberration-free line scan confocal Raman imager and type classification and distribution detection of microplastics.

An aberration-free line scanning confocal Raman imager (named AFLSCRI) is developed to achieve rapid Raman imaging. As an application example, various types and sizes of MPs are identified through Raman imaging combined with a machine learning algorithm. The system has excellent performance with a spatial resolution of 2 µm and spectral resolution of 4 cm−1. Compared to traditional point-scanning Raman imaging systems, the detection speed is improved by 2 orders of magnitude. The pervasive nature of MPs results in their infiltration into the food chain, raising concerns for human health due to the potential for chemical leaching and the introduction of persistent organic pollutants. We conducted a series of experiments on various types and sizes of MPs. The system can give a classification accuracy of 98% for seven different types of plastics, and Raman imaging and species identification for MPs as small as 1 µm in diameter were achieved. We also identified toxic and harmful substances remaining in plastics, such as Dioctyl Phthalate (DOP) residues. This demonstrates a strong performance in microplastic species identification, size recognition and identification of hazardous substance contamination in microplastics. [Display omitted] • Propose a new method to concurrently detect MPs types, sizes, and dopants. • Build a high-performance aberration-free line scan confocal Raman imager. • RNN is applied to Raman data cube for MPs detection. • This technology enables quick, non-destructive, high-resolution MPs detection. [ABSTRACT FROM AUTHOR]