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The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging

Bulletin of Environmental Contamination and Toxicology 2021 18 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Yanan Di, Hui Huang, Hui Huang, Hui Huang, Hui Huang, Hui Huang, Hui Huang, Yanan Di, Hui Huang, Hui Huang, Zehao Sun Zehao Sun Zhao Zhang, Zehao Sun Xiaojie Chen, Yanan Di, Hui Huang, Yanan Di, Yanan Di, Fengle Zhu, Yanan Di, Xiaochao Zhang, Yanan Di, Yanan Di, Shuyue Zhan, Yanan Di, Yanan Di, Shuyue Zhan, Zehao Sun Zehao Sun

Summary

Scientists used hyperspectral imaging combined with machine learning to distinguish between microplastic particles and microalgae in seawater samples. Developing reliable automated methods for identifying microplastics in complex environmental samples is critical for accurate contamination monitoring.

Polymers
Study Type Environmental

Based on the micro-hyperspectral imaging technique, spherical engineered microplastic (polyethylene, 10-45 μm) and microalgae (Isochrysis galbana) (4-7 μm) were identified. In transmittance mode of MHSI, micro image cubes from 400 to 1000 nm were obtained from slides containing MP and MA in thin seawater. Classifiers like Support Vector Machine (SVM(Radial Basis Function (RBF))), Least Squares Support Vector Machine (LSSVM(RBF)), k-Nearest Neighbors, etc. were adopted and compared to classify MP and MA. In order to expand the imaging range of micro imaging, image stitching technology was adopted. In allusion to the stitched image cube, SVM(RBF) is suggested for the identification of MA and MP, with recall and precision > 0.86. The above results demonstrate that the MHSI is a promising technique, which can detect MPs with particle size Limit of Detection of 10-45 μm, and it is potential to further expand this LOD.

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