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Papers
20 resultsShowing papers similar to Efficient screening of microplastics in soils using hyperspectral imaging in the short-wave infrared range coupled with machine learning – A laboratory-based experiment
ClearAccurate 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.
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.
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.
Coupling hyperspectral imaging with machine learning algorithms for detecting polyethylene (PE) and polyamide (PA) in soils.
Researchers combined hyperspectral imaging with machine learning algorithms to detect polyethylene and polyamide microplastics in soil samples. This rapid detection approach could support large-scale soil monitoring for microplastic contamination, which is important given that agricultural soils may accumulate plastics from mulch films, irrigation water, and sewage sludge.
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.
A Preliminary Study on the Utilization of Hyperspectral Imaging for the On-Soil Recognition of Plastic Waste Resulting from Agricultural Activities
Researchers explored the use of near-infrared hyperspectral imaging to detect and identify plastic waste in agricultural soils. They developed a classification model that could distinguish different types of plastic from soil and assess the degradation state of the material. The study demonstrates that hyperspectral imaging combined with chemometric analysis offers a rapid, non-destructive approach for monitoring plastic contamination in agricultural environments.
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.
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.
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.
Short-wave infrared hyperspectral imaging of microplastics: Effects of chemical and physical processes on spectral signatures and detection capabilities
Researchers evaluated short-wave infrared hyperspectral imaging for rapid microplastic detection and polymer identification, testing the effects of various physical and chemical weathering agents on spectral signatures and finding the technique effective for identifying multiple polymer types in complex samples.
Hyperspectral remote sensing as an environmental plastic pollution detection approach to determine occurrence of microplastics in diverse environments
Researchers tested whether hyperspectral remote sensing technology could detect microplastics mixed into different environmental surfaces like soil, water, concrete, and vegetation. Using near-infrared and short-wave infrared imaging, they achieved over 90% accuracy in detecting and classifying six common plastic types at concentrations as low as 0.15%. The study suggests that remote sensing could become a practical, large-scale tool for monitoring microplastic pollution across diverse environments.
Research on Soil Microplastics Detection Algorithm based on Hyperspectral Imaging Technology
Researchers developed a soil microplastic detection algorithm using hyperspectral imaging (400-1000 nm wavelength range) combined with three supervised classification approaches -- Support Vector Machine (SVM), Mahalanobis Distance (MD), and a third algorithm -- to enable convenient and efficient identification and classification of microplastic pollutants in soil.
Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil
Researchers evaluated whether hyperspectral imaging technology can reliably detect and quantify microplastics in soil under varying real-world conditions. They found that near-infrared imaging generally works well but is significantly affected by factors like soil moisture, microplastic color, and particle size. The study recommends sorting microplastics by size before analysis and further research into moisture effects, providing the first comprehensive evaluation of this emerging detection technology for soil monitoring.
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.
Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images
Researchers used near-infrared hyperspectral imaging combined with machine learning to classify nine types of microplastic particles, finding reliable results even for small particles on wet filters. This method could enable faster, automated identification of diverse microplastic types in environmental water samples.
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.
Issues with the detection and classification of microplastics in marine sediments with chemical imaging and machine learning
Researchers tested near-infrared hyperspectral imaging combined with four common machine learning algorithms to detect microplastics directly in marine sediment samples, finding that the method produced a large proportion of false positives and false negatives even in simple test conditions. The results raise serious concerns about the reliability of this widely used approach for environmental microplastic monitoring.
Rapid detection of microplastics in plastic-covered soils using FT-NIR and ATR-FTIR spectral data fusion
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.
VNIR and SWIR Hyperspectral Imaging for Microplastic detection on Soil
Researchers applied VNIR (400-1000 nm) and SWIR (1000-2000 nm) hyperspectral imaging to detect and identify seven types of cryo-milled microplastic polymers mixed into soil surfaces. Partial least squares regression models successfully distinguished polymer types, offering a non-destructive, rapid screening approach for identifying microplastics directly in soil environments.
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.