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Spectral data of PE and PP microplastics in soil (FT-NIR & ATR-FTIR)
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Researchers developed a dataset of FT-NIR and ATR-FTIR spectral data for polyethylene and polypropylene microplastics in soil, designed to support training and validation of a support vector regression model for rapid quantitative detection of microplastics using spectral fusion and machine learning.
This dataset supports the research titled “[Rapid Detection of Polyethylene and Polypropylene Microplastics in Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion],” which aims to develop a rapid method for quantitatively determining microplastics in soil based on spectral fusion and machine learning. The dataset contains all spectral and concentration data used for training and validating the support vector regression model.
More Papers Like This
Spectral data of PE and PP microplastics in soil (FT-NIR & ATR-FTIR)
AI summary Read the abstract
Researchers developed a dataset of FT-NIR and ATR-FTIR spectral data for polyethylene and polypropylene microplastics in soil, designed to support training and validation of a support vector regression model for rapid quantitative detection of microplastics using spectral fusion and machine learning.
Rapid detection of microplastics in plastic-covered soils using FT-NIR and ATR-FTIR spectral data fusion
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Researchers developed a rapid, non-destructive method to detect microplastics in agricultural soils by combining two infrared spectroscopy techniques (FT-NIR and ATR-FTIR) with machine-learning models. The fused spectral approach substantially outperformed either technique alone, detecting microplastics down to around 7 parts per million. Fast, accurate soil screening tools are critical for understanding and managing the growing microplastic contamination in farmland.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
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Scientists developed a new method to quickly detect tiny plastic particles in farm soil by combining two different light-based detection techniques. This method can accurately measure microplastic pollution in agricultural fields where plastic covers are used for growing crops. This matters because microplastics in farm soil can potentially enter our food chain through the fruits and vegetables we eat.
Predicting soil microplastic concentration using vis-NIR spectroscopy
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Researchers used visible and near-infrared (vis-NIR) spectroscopy to predict microplastic concentrations in soil samples, developing calibration models that could estimate contamination levels directly from spectral measurements without extensive sample preparation. The approach offers potential for faster and more scalable monitoring of microplastic pollution in agricultural and natural soils.
Hyperspectral imaging for detection and quantification of microplastics in soil: development of a comprehensive spectral library and a data analysis methodology
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Researchers built a hyperspectral imaging framework using visible-near-infrared light and a custom spectral-difference algorithm to detect and quantify PE, PP, PET, and ABS microplastics in soil without chemical labeling, establishing a quantitative calibration that links optical reflectance signals to mass-based concentrations in parts per million.
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