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Identification of Microplastics Based on the Fractal Properties of Their Holographic Fingerprint
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Researchers developed an AI-enabled holographic imaging approach to identify microplastics in water using the fractal properties of their holographic fingerprints, offering a fast, label-free identification method.
Water plastic pollution is a serious problem affecting sealife, marine habitats, and the food chain. Artificial intelligence-enabled coherent imaging has recently shown exciting advances in the field of environmental monitoring, and portable holographic microscopes are good candidates to map the microparticles content of marine waters. The “holographic fingerprint” due to coherent light diffraction is rich in information, fully encoded into the complex wavefront scattered by the sample. Hence, proper analysis of the wavefronts reconstructed from digital holograms can unlock new possibilities in the fields of diagnostics and environmental monitoring. Fractal geometry well describes natural objects and allows inferring added-value information on the way these fill 2D spaces and 3D volumes. The most abundant micron-scale class of objects that populate marine waters consists of microalgae named diatoms, which are of interest as bioindicators of water quality. Here we investigate the fractal properties of holographic patterns of diatoms and microplastics, considering a heterogeneous mixture of five types of plastic materials and 55 different species of microalgae. We show that, different from the case of weak scattering objects, a small set of fractal parameters is able to characterize these two large ensembles. As an applicative example, we carry out classification tests to show the possibility to identify the two classes with high accuracy. This new holographic fractal description of scattering micro-objects could be used in the near future for in situ automatic mapping of microplastic pollutants and for taxonomy of diatoms as water quality bioindicators, screened onboard holographic systems.
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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.
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Researchers developed a digital holographic imaging system combined with deep learning to automatically classify and analyze microplastic particles in water samples. Automated imaging and AI-based identification could significantly speed up and standardize microplastic monitoring, reducing the labor-intensive manual counting currently required.
Automatic Detection of Microplastics by Deep Learning Enabled Digital Holography
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Researchers developed a digital holography system combined with deep learning to automatically detect and identify microplastics in water without manual image analysis. The system processes raw holographic images directly, offering a faster and more scalable approach to microplastic monitoring in environmental samples.
Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
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Researchers developed a machine learning system that uses holographic 3D images to automatically classify microplastics in water samples, distinguishing them from other microscopic particles with high precision. Current microplastic monitoring is slow and labor-intensive, so automated detection tools are essential for large-scale environmental surveillance. This approach could significantly speed up the monitoring of microplastic pollution in aquatic environments.
Characterization of microplastics by holographic features for automatic detection in heterogeneous samples
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A holographic imaging approach was developed and evaluated for automated detection and characterization of microplastics in liquid samples, using holographic features to distinguish plastic particles from other matter. The method offers a path toward rapid, high-throughput microplastic identification in complex water samples without laborious manual microscopy.
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