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How to identify colorless microplastic directly in the beach sand in a few minutes?
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Researchers developed a high-throughput near-infrared hyperspectral imaging technique to automatically detect and identify colorless microplastics directly in beach sand within a few minutes. The chemometric classification model can identify multiple plastic polymer types without requiring chemical pre-treatment.
Development of a high throughput screening technique based on line-scan near-infrared hyperspectral imaging (HSI-NIR) and chemometrics to automatically detect and identify microplastics. Chemometrics were performed with soft independent modelling of class analogy (SIMCA). The supervised classification model was built for microplastic automated identification.
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A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics
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Researchers developed a near-infrared hyperspectral imaging method combined with chemometric analysis for rapid, high-throughput identification of microplastic types in mixed samples, achieving high classification accuracy and offering a faster alternative to FTIR and Raman methods for routine monitoring.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
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Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images
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Researchers used near-infrared hyperspectral imaging combined with machine learning to classify nine types of microplastic particles, finding reliable results even for small particles on wet filters. This method could enable faster, automated identification of diverse microplastic types in environmental water samples.
Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
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Researchers explored the use of hyperspectral imaging technology to detect and identify different types of plastic debris on beach sand. The method can distinguish between various polymer types, supporting more efficient recycling and cleanup operations. The study demonstrates a non-contact detection approach that could help prevent further degradation of shoreline plastics into microplastics.
Microplastics characterization by hyperspectral imaging in the SWIR range
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Researchers developed a hyperspectral imaging methodology operating in the short-wave infrared range (1000-2500 nm) combined with chemometric classification to rapidly identify polymer types in microplastic samples collected from marine environments. The non-destructive approach enabled polymer characterisation across samples from multiple geographical regions without requiring chemical pre-treatment.
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