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61,005 resultsShowing papers similar to Rapid detection of microplastics in plastic-covered soils using FT-NIR and ATR-FTIR spectral data fusion
ClearRapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
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.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
Scientists developed a faster way to detect tiny plastic particles in farm soil by combining two different scanning methods. This new technique can accurately measure microplastic pollution in agricultural fields where plastic covers are used to help crops grow. This matters because microplastics in farm soil can potentially enter our food supply, so having better detection methods helps us monitor and control this type of pollution.
Spectral data of PE and PP microplastics in soil (FT-NIR & ATR-FTIR)
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.
Spectral data of PE and PP microplastics in soil (FT-NIR & ATR-FTIR)
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.
Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging
Researchers combined short-wave infrared hyperspectral imaging with machine learning algorithms to detect low concentrations of polyamide and polyethylene microplastics in soil samples, achieving accurate classification with implications for fast, non-destructive screening of agricultural land for plastic contamination.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
Predicting soil microplastic concentration using vis-NIR spectroscopy
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.
Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment
Researchers tested short-wave infrared hyperspectral imaging combined with machine learning to detect three types of microplastics in soil, finding it could identify elevated contamination but was not sensitive enough for typical environmental background levels. The technique shows most promise for screening heavily polluted sites like landfills and industrial areas.
Toward high-precision analysis of soil micro-and nanoplastics: A review of spectroscopy and machine learning approaches
Researchers reviewed multiple spectroscopy techniques — including infrared, Raman, and hyperspectral imaging — combined with machine learning as faster, cheaper alternatives to traditional methods for detecting microplastics and nanoplastics in soil. While promising, key challenges remain including poor detection of nanoplastics, limited real-world validation, and detection limits that often miss environmentally relevant concentrations.
Automated identification and quantification of invisible microplastics in agricultural soils
Researchers developed an automated method combining laser direct infrared and FTIR spectroscopy to identify microplastics in agricultural soils, revealing that particles smaller than 500 micrometers account for over 96% of soil microplastics that are invisible to traditional visual inspection.
Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection
Researchers applied near-infrared spectroscopy with machine learning algorithms to rapidly measure soil nitrogen content. While focused on agricultural management rather than microplastics, spectroscopic methods like near-infrared are also used for detecting microplastics in soil samples.
Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil
Researchers developed a rapid, non-destructive method using visible and near-infrared spectroscopy to quantify microplastics in farm soil. The study suggests this approach could overcome the limitations of current extraction-based methods, which are time-consuming and prone to errors and biases.
High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil
High-throughput near-infrared spectroscopy (NIRS) was evaluated for detecting and quantifying microplastics in soil samples, finding that it could rapidly identify multiple polymer types without time-consuming sample preparation. The method offers potential for scaling up microplastic monitoring in terrestrial environments where conventional analytical methods are too slow for large sample numbers.
Innovative approach for determining polypropylene microplastics pollution in calcareous soils: Vis-NIR spectroscopy
Researchers demonstrated that visible and near-infrared (Vis-NIR) spectroscopy combined with statistical modeling can accurately detect and quantify polypropylene microplastics in agricultural calcareous soils, with a model accuracy of R² = 0.91. This is promising because it could enable rapid, low-cost field screening of soil microplastic contamination without expensive laboratory analysis.
Application of a Hybrid Fusion Classification Process for Identification of Microplastics Based on Fourier Transform Infrared Spectroscopy
A hybrid machine learning approach was developed to improve the identification of microplastics using infrared spectroscopy, overcoming the limitations of standard library-matching methods. The new method better handles weathered or contaminated microplastic particles, which are harder to identify using conventional approaches.
Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil
Researchers applied hyperspectral imaging technology combined with machine learning to rapidly screen and classify microplastics in farmland soil samples, demonstrating an efficient non-destructive identification method for soil microplastic contamination.
Convolutional neural network for soil microplastic contamination screening using infrared spectroscopy
Researchers trained a convolutional neural network on visible-near-infrared spectra to classify soil samples by degree of microplastic contamination, using concentrations from industrial areas around metropolitan Sydney as a baseline. The model accurately identified uncontaminated samples and improved classification of highly contaminated samples as the number of contamination classes increased, with transfer learning further enhancing performance.
An investigation on the applications of advanced Infrared Spectroscopy, Spectral Imaging and Machine Learning for Polymer Characterization, including microplastics
This study integrated advanced infrared spectroscopy, spectral imaging, chemometrics, and machine learning to identify and characterize microplastics and polymer degradation products. The combination of techniques improved both the accuracy and throughput of MP analysis compared to conventional methods.
Application of hyperspectral and deep learning in farmland soil microplastic detection
Hyperspectral imaging combined with deep learning was applied to detect and classify microplastics in farmland soil, offering a non-destructive, rapid alternative to time-consuming chemical extraction methods. The model achieved high classification accuracy across polymer types, demonstrating the potential for field-deployable microplastic monitoring in agricultural settings.
A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics
Hyperspectral imaging combined with chemometrics was demonstrated as a novel way to rapidly detect and map multiple types of microplastics in soil samples, identifying particles of different polymer types based on their spectral signatures. The approach could enable faster and more spatially detailed monitoring of microplastic contamination in agricultural and environmental soils.
Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
Towards a fast and generalized microplastic quantification method in soil using terahertz spectroscopy
Researchers compared terahertz and near-infrared spectroscopy for quantifying microplastics in soil, finding that terahertz spectroscopy offered a faster and more accurate approach than NIR for distinguishing household microplastics from standard reference polymers in soil matrices.
Hyperspectral detection of soil microplastics via multimodal feature fusion and a dual-path attention residual convolutional network
A hyperspectral imaging approach combined with multimodal deep learning was developed to detect microplastics in soil, achieving high accuracy in identifying plastic particles against complex soil backgrounds. The method offers a faster, less destructive alternative to traditional chemical extraction and spectroscopy for soil monitoring.
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil
Researchers used non-destructive hyperspectral imaging in visible-near infrared and short-wave infrared ranges to detect microplastics on soil surfaces. Using seven different cryo-milled microplastic polymers and partial least squares analysis, the study demonstrates that hyperspectral imaging can identify microplastics in soil without the complicated, time-consuming steps required by conventional detection methods.