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Papers
20 resultsShowing papers similar to Microplastic detection in soil by THz Time-Domain hyperspectral imaging combined with unsupervised learning analysis
ClearStudy on Rapid Quantitative Detection of Soil MPs Based on Terahertz Time-Domain Spectroscopy
Researchers developed a rapid method for detecting and quantifying microplastics in soil using terahertz time-domain spectroscopy combined with machine learning algorithms. The classification models achieved high accuracy in identifying different types of microplastics including polyethylene, polystyrene, and polypropylene. The study suggests that terahertz spectroscopy could provide a faster and more efficient alternative to current methods for monitoring microplastic contamination in agricultural soils.
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.
Optical parameters extraction of soil and its microplastics contamination using terahertz spectroscopy
Researchers used terahertz spectroscopy to detect and quantify low-density polyethylene microplastics mixed into soil at different concentrations, finding that the technique could distinguish contaminated from clean soil based on changes in refractive index and signal attenuation. Terahertz spectroscopy is non-destructive and rapid, making it a potentially valuable tool for in-field soil microplastic screening without the need for laboratory extraction.
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.
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.
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.
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.
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.
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.
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.
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.
Development of a Compact and Portable Terahertz Imaging System for Industrial Applications
Researchers developed a compact, portable terahertz imaging device suitable for use outside the laboratory, demonstrating its ability to detect microplastics in soil among a range of other applications. While microplastic detection is one of several uses tested, the availability of low-cost, field-deployable detection technology could support faster and broader environmental monitoring of microplastic contamination.
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.
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.
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.
Research on Identification and Classification Methods for Soil Microplastics in Hyperspectral Detection
Hyperspectral imaging was tested as a rapid, large-area detection method for identifying and classifying microplastics in soil, offering an alternative to time-consuming particle-by-particle Raman or FTIR spectroscopy. The approach could allow researchers to map microplastic distribution across soil samples far more efficiently. Faster detection technology is important for expanding the geographic scope of soil microplastic monitoring and for assessing contamination in agricultural land.
Detection of Microplastic in Salts Using Terahertz Time-Domain Spectroscopy
Researchers demonstrated that terahertz spectroscopy can detect microplastics embedded in table salt at different concentrations. This technology could offer a new non-destructive method for screening food products for microplastic contamination.
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.
A Microwave-Based Sensing Platform for Microplastic Detection and Quantification: A Machine Learning-Assisted Approach
Researchers developed a low-cost microwave sensor combined with machine learning to detect and quantify microplastics in water and identify polymer types in unknown samples. The platform achieved the highest sensitivity reported among microwave-based approaches for microplastic detection, offering a promising low-cost alternative to spectroscopy-based methods.
Self-Supervised Hierarchical Dilated Transformer Network for Hyperspectral Soil Microplastic Identification and Detection
A self-supervised hierarchical dilated transformer neural network was developed for automated classification of microplastic images, achieving high accuracy across multiple polymer types. The deep learning approach reduces the labor-intensive manual identification step in microplastic analysis workflows.