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Papers
20 resultsShowing papers similar to Study on Rapid Quantitative Detection of Soil MPs Based on Terahertz Time-Domain Spectroscopy
ClearTowards 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.
Microplastic detection in soil by THz Time-Domain hyperspectral imaging combined with unsupervised learning analysis
Researchers applied terahertz time-domain hyperspectral imaging combined with multiple unsupervised machine-learning algorithms to detect and spatially map low-density polyethylene microplastics in soil, demonstrating that all five methods consistently separated plastic from soil without requiring labeled training data, establishing a reference-free detection approach.
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.
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.
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.
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.
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.
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.
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.
Characterizations of high-density polyethylene by terahertz time-domain spectroscopy
Researchers characterized the optical properties of high-density polyethylene (HDPE) particles using terahertz time-domain spectroscopy and found the method can distinguish different particle sizes and filler contents. The technique can also detect how HDPE adsorbs other substances onto its surface. Terahertz spectroscopy could be developed as a rapid, non-destructive tool for identifying and characterizing HDPE microplastics in environmental samples.
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.
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.
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.
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.
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.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers used 3D laser scanning confocal microscopy paired with machine learning to detect microplastics in agricultural soil. The method successfully identified low-density polyethylene particles from mulching films, providing a faster and more precise tool for tracking plastic contamination in farmland.
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.
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.