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"SensNexus Spectral Dataset (SensNexusDat)"
Summary
Scientists created a detailed spectral "fingerprint" library by testing coastal soil contaminated with common plastic particles (like those from bottles and packaging), using a light-based scanning method instead of slower lab tests. This dataset can help researchers build faster, cheaper tools to detect microplastic pollution in soil, which matters because microplastics in soil can enter our food and water supply, making early detection an important step in understanding and reducing our exposure to them.
"Diffuse reflectance spectroscopy in the visible, near-infrared, and short-wave infrared (Vis\u2013NIR\u2013SWIR) ranges has emerged as a promising approach for detecting and quantifying microplastic (MP) contamination in soils, offering rapid, non-destructive analysis. Despite growing interest, published spectral datasets in this domain remain scarce and are generally limited to pristine polymer pellets or environmentally collected materials measured independently from soil matrices, or to studies involving non-coastal and non-tropical soils. This gap constrains the development and validation of predictive models tailored to the spectral complexity of real-world contaminated environments. This dataset was specifically constructed to support the training and validation of chemometric and machine learning models for MP quantification in soil using Vis\u2013NIR\u2013SWIR spectroscopy. It provides high-resolution spectral data (350\u20132500 nm) from a Brazilian coastal soil artificially contaminated under controlled laboratory conditions with particles of four polymer types, polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), and polyvinyl chloride (PVC), encompassing both pristine polymers, intended to support model training, and colored post-consumer plastics, intended to support model validation under more realistic spectral conditions. The dataset addresses an underrepresentation of coastal and tropical soil environments in the MP spectroscopy literature and enables comparative analyses between pure polymers and colored, additive-containing post-consumer materials. Its spectral coverage and high resolution make it suitable for chemometric modeling, spectral library development, machine learning applications, and cross-dataset interoperability studies, with potential applications in proximal and remote sensing-based MP detection, digital soil mapping, and coastal environmental monitoring."