A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers

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.