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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Detection Methods Environmental Sources Marine & Wildlife Sign in to save

Identification of Microplastics in Soils Using 2D Geometric Shape Descriptors

AGILE GIScience Series 2021 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Irada Ismayilova, Tabea Zeyer, Sabine Timpf

Summary

Researchers applied 2D geometric shape descriptors and Random Forest machine learning to distinguish microplastic particles from soil particles in microscopy images. The results demonstrated promising potential for automated microplastic identification in soil samples, which are more challenging to analyze than water samples.

Abstract. Microplastics (MP), until now mostly studied in aquatic ecosystems, are also largely polluting terrestrial ecosystems, especially soil systems. Overall, there is a lack of robust and fast methods to identify, separate and eliminate MPs from soils. This paper is a first attempt to use 2D shape descriptors and Random Forest Machine Learning method in order to discriminate soil and MP particles. The results of this study demonstrate promising potential of the Machine Learning approach and shape descriptors in this relatively new scientific field of determining MPs in soils.

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