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Processed NIR-HSI and self-organizing map dataset for microplastic classification on sandy substrates
Summary
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.