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Processed NIR-HSI and self-organizing map dataset for microplastic classification on sandy substrates
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Scientists have developed a camera-based scanning technique that can spot and map tiny plastic bits (microplastics) hiding in sand, without digging through it or destroying samples. This dataset provides the underlying reference data and computer models used to identify different plastic types, which could help researchers monitor beach pollution more efficiently, an important step since microplastics can end up in the food chain and, eventually, in us.
Processed supporting dataset for the manuscript 'Hyperspectral imaging and self-organizing map approach for non-destructive monitoring of microplastic contamination in sandy substrates.' The collection contains processed reference spectra for PET, PE, PP, PS, and sand; trained self-organizing map inputs and outputs; percent-based expansion tolerance colormaps; and representative projection and classification structures used for polymer mapping and surface-coverage estimation. It is not a complete archive of all raw hyperspectral cubes or every intermediate variable.
More Papers Like This
Processed NIR-HSI and self-organizing map dataset for microplastic classification on sandy substrates
AI summary Read the abstract
Scientists have developed a camera-based scanning technique that can detect and map tiny plastic pollution (microplastics) on sandy beaches without disturbing the sand or destroying samples. This matters because microplastics on beaches can wash into oceans, work their way into seafood, and eventually end up in the food we eat, better detection tools like this could help track and reduce contamination before it spreads. Note that this specific dataset is a technical resource (processed reference data) supporting the method, rather than a study of health effects itself.
Hyperspectral imaging and self-organizing map approach for non-destructive monitoring of microplastic contamination in sandy substrates
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Scientists developed a camera-based scanning technique that can spot and measure tiny plastic fragments, including pieces smaller than 1mm, buried in sand, without destroying the samples. This matters because microplastics on beaches and in soil are notoriously hard to detect and measure accurately, and better detection tools are a key first step toward understanding how much of this pollution surrounds us and eventually ends up in our bodies and food chain.
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.
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.
Analysis of marine plastic fragments using a portable near-infrared instrument: The effect of scanning position on the classification of polymer type
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Scientists tested a handheld scanning device that can quickly identify what type of plastic washes up on beaches, without needing a lab. This matters because knowing the exact plastic type helps determine if it's safe to recycle or if it might break down into harmful microplastics that can end up in our water, food, and eventually our bodies—making cleanup efforts smarter and more efficient.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.