We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Unveiling microplastic spectral signatures under weathering and digestive environments through shortwave infrared hyperspectral sensing
ClearShort-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.
Enhanced Identification of Weathered Plastics Through the Improvement of Infrared Spectral Libraries
Researchers developed an improved infrared spectral library specifically designed to identify weathered and degraded plastics that conventional libraries often misidentify. The new library increased match rates by 7.3% for thermally oxidized plastics and improved identification of mechanically abraded samples, addressing a significant gap in accurate microplastic detection and environmental risk assessment.
Rapid and direct detection of small microplastics in aquatic samples by a new near infrared hyperspectral imaging (NIR-HSI) method
Researchers developed a rapid near-infrared hyperspectral imaging method capable of detecting and chemically identifying small microplastics (down to a few hundred micrometers) in aquatic samples faster and with less labor than traditional spectroscopy approaches.
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.
Developing and testing a workflow to identify microplastics using near infrared hyperspectral imaging
Researchers developed a near-infrared hyperspectral imaging workflow with an open spectral database to rapidly identify microplastics by polymer type, achieving over 88% accuracy for polypropylene, polyethylene, PET, and polystyrene particles larger than 500 micrometers.
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.
Impact of weathering on the chemical identification of microplastics from usual packaging polymers in the marine environment
The impact of environmental weathering on the chemical identification of common microplastics was investigated, examining how UV radiation, mechanical abrasion, and microbial activity alter the spectroscopic signatures used for polymer identification. Weathered plastics were harder to correctly identify than pristine ones, highlighting the need for reference libraries that include aged material.
Microplastics characterization by hyperspectral imaging in the SWIR range
Researchers developed a hyperspectral imaging methodology operating in the short-wave infrared range (1000-2500 nm) combined with chemometric classification to rapidly identify polymer types in microplastic samples collected from marine environments. The non-destructive approach enabled polymer characterisation across samples from multiple geographical regions without requiring chemical pre-treatment.
Weathering-independent differentiation of microplastic polymers by reflectance IR spectrometry and pattern recognition
Researchers developed a weathering-independent method for identifying microplastic polymer types using reflectance infrared spectrometry combined with pattern recognition techniques including principal components analysis and classification trees, demonstrating reliable polymer differentiation even when field samples are weathered or biofouled.
Spectrometric Detection Of Microplastics In The Environment: A Novel Approach Using Hyperspectral Imaging System
This study developed a novel spectrometric approach to detect microplastics in environmental samples, combining spectral analysis with machine learning classification. The method enabled rapid, accurate identification of multiple polymer types without extensive sample preparation.
Characterization of microplastics on filter substrates based on hyperspectral imaging: Laboratory assessments
Researchers evaluated near-infrared hyperspectral imaging as a method for characterizing microplastics on filter substrates, finding that 11 plastic polymers exhibited distinct spectral features at specific wavelength ranges enabling automatic identification, and also assessed the spectral compatibility of 11 different filter substrate materials.
A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics
Researchers developed a near-infrared hyperspectral imaging method combined with chemometric analysis for rapid, high-throughput identification of microplastic types in mixed samples, achieving high classification accuracy and offering a faster alternative to FTIR and Raman methods for routine monitoring.
An effective strategy for the monitoring of microplastics in complex aquatic matrices: Exploiting the potential of near infrared hyperspectral imaging (NIR-HSI)
Researchers developed a near infrared hyperspectral imaging (NIR-HSI) method for rapid monitoring of microplastics in complex marine matrices, demonstrating effective detection and polymer identification that overcomes the time and cost limitations of conventional spectroscopic analysis approaches.
μ-FTIR Reflectance Spectroscopy Coupled with Multivariate Analysis: A Rapid and Robust Method for Identifying the Extent of Photodegradation on Microplastics
Researchers developed a faster, more sensitive method for identifying weathered microplastics using infrared reflectance spectroscopy combined with statistical analysis. The technique can classify different plastic types and assess their level of sun damage without complex data preprocessing. The approach could improve the speed and accuracy of environmental microplastic monitoring, particularly for particles that have been altered by exposure to sunlight.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples
Hyperspectral infrared imaging is an effective method for finding and characterizing microplastics in environmental samples, and this paper explores analytical approaches for extracting useful information from the large datasets it generates. Better analytical tools make it faster and more accurate to identify and classify microplastics in real-world samples.
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.
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.
Hyperspectral ultraviolet to shortwave infrared characteristics of marine-harvested, washed-ashore and virgin plastics
Researchers characterized the hyperspectral optical properties of marine-harvested plastic litter across ultraviolet to shortwave infrared wavelengths, generating spectral signatures needed to support remote sensing detection of floating plastic debris. The spectral library produced contributes to developing satellite and airborne monitoring systems for large-scale ocean plastic surveillance.
Fast identification of microplastics in complex environmental samples by a thermal degradation method
Researchers developed a fast identification method for microplastics in complex environmental samples using thermal analysis, offering a high-throughput alternative to spectroscopic techniques for polymer identification.
Spectroscopic Identification of Environmental Microplastics
Scientists developed a machine learning classifier that identifies the chemical type of environmental microplastic samples from spectral data with over 97% accuracy, even for samples from unknown sources. Automated spectral identification tools are critical for scaling up microplastic monitoring across large environmental datasets.
Detection and identification of microplastics directly in water by hyperspectral imaging
Researchers used hyperspectral imaging to identify different types of microplastics mixed together in water, demonstrating that the technique can distinguish polymer types based on their spectral signatures. This non-destructive, real-time method could improve the speed and accuracy of microplastic monitoring in water samples.
Characterizing photochemical ageing processes of microplastic materials using multivariate analysis of infrared spectra
Researchers tracked how four common plastic types weather under UV light and sunlight over six months, using infrared spectroscopy and multivariate analysis to characterize surface chemistry changes. They found that polypropylene weathered fastest, while all plastics showed photooxidation at different rates depending on light source. The study proposes a multivariate spectral approach as a more broadly applicable method than the traditional carbonyl index for assessing microplastic aging.
Machine learning based workflow for (micro)plastic spectral reconstruction and classification
A machine learning pipeline combining two spectral reconstruction models with four classification algorithms can identify microplastic polymer types from spectral data with up to 98% accuracy on processed spectra. Applied to real environmental samples, the best model achieved 71% top-one accuracy and over 90% top-three accuracy. Automated, high-accuracy microplastic identification tools are critical for scaling up environmental monitoring and making large-scale surveys practical.