Holographic Imaging and Machine Learning for Microplastic Size and Shape Analysis in Water

Microplastic Identification via Holographic Imaging and Machine Learning

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Researchers combined holographic imaging with machine learning algorithms to automatically identify and classify microplastics in water samples, achieving accurate particle detection without manual microscopy. This automated approach could significantly speed up microplastic monitoring in environmental samples.

HMPD: A Novel Dataset for Microplastics Classification with Digital Holography

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Researchers developed HMPD, a novel digital holography dataset for training machine learning models to classify microplastic microfibers shed from synthetic textiles during washing. Automated, high-throughput detection methods like this are needed to monitor laundry-derived microfibers — one of the largest contributors to environmental microplastic loads — at scale in wastewater and environmental samples.

Searching and identifying microplastics in marine environment by digital holography⋆

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Researchers demonstrated that digital holography can non-invasively detect and reconstruct the three-dimensional shapes of different microplastic types in water, including in the presence of biological matter such as diatoms, offering a promising new optical tool for environmental monitoring.

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

Defining Irregular Microplastics: A Machine Learning Approach for Morphometric Characterization

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Researchers applied a machine learning decision-tree classifier to hyperspectral images of 129 nano- and microplastic particles and identified five morphometric descriptors—circularity, roundness, solidity, long diameter, and perimeter-area ratio—that together distinguish irregular particles from spherical and fibrous forms with 96% accuracy, providing a standardized definition for the most environmentally prevalent but least-defined shape class.