Summary
As microplastic (MP) particles continue to spread globally, their pervasive presence is increasingly problematic. Analyzing MPs in matrices as varied as soil, river water, and biosolid fertilizers is critical, as these matrices directly impact the food sources of plants, animals, and humans. Current analytical methods for quantifying and identifying MPs are limited due to labor-intensive extraction processes and the time and effort required for counting and analysis. Recently, Machine Learning (ML) has been introduced to the analysis of MPs in complex matrices, significantly reducing the need for extensive extraction and increasing analysis speeds. This work aims to illuminate various ML techniques for new researchers entering this field. It highlights numerous examples in the application
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