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Article
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Tier 2
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Original research — experimental, observational, or case-control study. Direct primary evidence.
Detection Methods
Marine & Wildlife
Policy & Risk
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A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers
Analytical Methods
2019
128 citations
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Benedikt Hufnagl,
Dieter Steiner,
Elisabeth Renner,
Martin G. J. Löder,
Christian Laforsch,
Hans Lohninger
Summary
Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
A new yet little understood threat to our ecosystems is microplastics.