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Papers
61,005 resultsShowing papers similar to Towards non-contact pollution monitoring in sewers with hyperspectral imaging
ClearTowards non-contact pollution monitoring in sewers with hyperspectral imaging
Researchers developed a non-contact hyperspectral imaging system to monitor water quality inside sewers without direct contact with raw wastewater. This approach could enable continuous monitoring of plastic and other contaminants in urban drainage systems without the fouling problems that plague conventional sensors.
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.
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.
Hyperspectral Imaging and Data Analysis for Detecting and Determining Plastic Contamination in Seawater Filtrates
Researchers tested whether hyperspectral imaging combined with multivariate data analysis could detect and identify plastic particles on filters from seawater samples, finding the method could locate plastic contamination and distinguish polymer types. This approach could offer a faster and more automated alternative to manual microscopy for environmental microplastic monitoring.
Hyperspectral imaging systems (HSI) and chemometric methods for the rapid and direct detection of microplastics
Researchers developed a near-infrared hyperspectral imaging method using an automated normalized difference image strategy to rapidly detect microplastics in surface water and mussel tissue samples without requiring visual pre-sorting or extensive purification. They also advanced the characterization of tire and road wear particles by co-registering X-ray fluorescence data with visible and near-infrared spectroscopy, enabling rapid identification of particles by chemical composition.
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.
Hyperspectral imaging systems (HSI) and chemometric methods for the rapid and direct detection of microplastics
Researchers evaluated hyperspectral imaging (HSI) systems combined with chemometric analysis methods as a rapid, direct detection approach for microplastics on filters, avoiding the time-consuming visual pre-sorting and sample purification steps required by conventional spectroscopic methods. The study demonstrated that HSI can identify and map microplastic particles across diverse sample matrices faster and with reduced contamination risk compared to traditional FTIR and Raman approaches.
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.
Hyperspectral imaging: An early systematic review of emerging applications for rapid microplastic analysis
This systematic review examines the emerging use of hyperspectral imaging technology for detecting and analyzing microplastics in environmental samples. Better detection methods matter for human health because accurately measuring microplastic contamination in water, food, and air is essential for understanding our true level of exposure and developing effective strategies to reduce it.
Hyperspectral imaging for identification of irregular-shaped microplastics in water
Researchers demonstrated a method using hyperspectral imaging to detect and identify ten different types of microplastics directly in water samples. By selecting fourteen specific wavelengths and computationally removing water interference, they could distinguish between plastic types without the labor-intensive sample preparation that current methods require. The technique could make routine microplastic water monitoring faster and more accessible for environmental testing.
Hyperspectral Imaging as a Potential Online Detection Method of Microplastics
Researchers evaluated hyperspectral imaging (HSI) as a potential online detection method for microplastics in aquatic environments, assessing its ability to rapidly identify polymer types. The study found HSI shows strong promise for fast polymer identification, though improvements in processing speed are needed for real-time monitoring applications.
Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology
Researchers developed a hyperspectral imaging technique for rapid detection and identification of microplastics in seawater, demonstrating it could analyze multiple particles simultaneously and significantly reduce the time burden compared to traditional individual-particle identification protocols.
Towards Robust River Plastic Detection: Combining Lab and Field-based Hyperspectral Imagery
Researchers combined lab-based and field-based hyperspectral imagery (1150-1675 nm) to develop a more robust method for detecting macroplastics in river environments, using riverbank-harvested plastics in controlled laboratory experiments and on the banks of the Rhine River to improve the transferability of spectral identification to natural settings.
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 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.
Instant plastic waste detection on shores using laser-induced fluorescence and associated hyperspectral imaging
Researchers demonstrated the use of laser-induced fluorescence combined with hyperspectral imaging for rapid detection of plastic waste on shorelines. The study suggests this technology could enable efficient, real-time monitoring of plastic pollution on beaches and coastal areas through remote sensing approaches.
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.
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.
Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System
This study investigated hyperspectral imaging for non-destructive detection of explosive traces, demonstrating the potential of reflectance-based systems for identifying materials by their spectral signatures. Hyperspectral and Raman-based approaches are also being developed for identifying microplastic polymer types in environmental samples.
A sustainable analytical workflow for microplastic detection and typification via NIR-HSI: Validation through sea salt analysis
Researchers developed a sustainable analytical workflow for detecting and classifying microplastics using near-infrared hyperspectral imaging combined with chemometrics. The study validated the method through sea salt analysis, demonstrating a rapid, non-destructive, and solvent-free approach that aligns with green analytical chemistry principles for environmental microplastic monitoring.
Detection of microplastics in sea salt using hyperspectral imaging and machine learning methods: Pollution control in the Mediterranean sea as a case study
Hyperspectral imaging combined with machine learning was used to detect and classify microplastics in Mediterranean sea salt samples, demonstrating a rapid, non-destructive analytical approach with potential for routine quality control in the food industry.
Laboratory Hyperspectral Image Acquisition System Setup and Validation
Researchers designed and validated a custom laboratory hyperspectral imaging acquisition system for capturing precise spectral data across diverse sample types, establishing a foundation for developing algorithms applicable to environmental monitoring including microplastic detection.
Hyperspectral estimation of mercury content of soil in Oasis city in arid zones of China
Researchers used hyperspectral remote sensing to develop a faster and cheaper method for estimating mercury contamination in soil near Urumqi, China. They tested multiple spectral transformation methods and found that certain approaches combined with machine learning models could accurately predict soil mercury levels. The technique offers a practical alternative to traditional laboratory-based soil testing for large-scale environmental monitoring.
Eliminating the interference of water for direct sensing of submerged plastics using hyperspectral near-infrared imager
Researchers developed a hyperspectral imaging technique — which captures light across many wavelengths invisible to the human eye — that can detect different types of plastic submerged in water by filtering out water's interference with the light signal. The method successfully identified nine common plastic types at depths up to 15 mm, offering a promising tool for detecting microplastics in aquatic environments from lab benches or aircraft.