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Hyperspectral Imaging as a Potential Online Detection Method of Microplastics

Bulletin of Environmental Contamination and Toxicology 2020 62 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Hui Huang, Chunfang Zhang Chunfang Zhang Hui Huang, Hui Huang, Hui Huang, Hui Huang, Hui Huang, Hui Huang, Chunfang Zhang Hui Huang, Junaid Ullah Qureshi, Zehao Sun, Zehao Sun, Junaid Ullah Qureshi, Junaid Ullah Qureshi, Shuchang Liu, Shuchang Liu, Junaid Ullah Qureshi, Junaid Ullah Qureshi, Junaid Ullah Qureshi, Chunfang Zhang Zehao Sun, Hui Huang, Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Hangzhou Wang, Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Chunfang Zhang Zehao Sun, Zehao Sun, Chunfang Zhang

Summary

Researchers evaluated hyperspectral imaging (HSI) as a potential online detection method for microplastics in aquatic environments, assessing its ability to rapidly identify polymer types. The study found HSI shows strong promise for fast polymer identification, though improvements in processing speed are needed for real-time monitoring applications.

Microplastic pollution in aquatic environment has raised concern and as a result a number of studies have recently been published to find solutions for its rapid increase. Different methods have been proposed for microplastic identification. Spectral imaging shows a lot of promise for polymer identification; however, the identification time needs to be improved. Hyperspectral imaging (HSI) combined with chemometric analysis can reduce the identification times. In this study, we provide a review of recent studies related to polymer identification using HSI with a focus on the adopted classification algorithm and its factors for the online implementation of HSI. Furthermore, we review the limit of detection by HSI and the effect of particle size on classification accuracy. Additionally, performance of this method for various types of samples is also discussed. We conclude that HSI is possible to be a fast alternative for online microplastic detection.

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