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61,005 resultsShowing papers similar to Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology
ClearDetection 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.
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 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.
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.
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.
Rapid shipboard measurement of net-collected marine microplastic polymer types using near-infrared hyperspectral imaging
Researchers developed a rapid near-infrared hyperspectral imaging method for identifying polymer types in ship-collected marine microplastic samples, achieving results in minutes compared to hours for conventional methods and enabling higher-throughput ocean monitoring.
Rapid identification of microplastics through spectral reconstruction from RGB images
Researchers developed a method to generate hyperspectral bands and extract spectral signatures from standard RGB images, applying spectral reconstruction to streamline microplastic identification. Experimental results validated the approach's efficacy in enabling comprehensive spectroscopic analysis while significantly reducing imaging time compared to traditional hyperspectral acquisition methods.
The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging
Scientists used hyperspectral imaging combined with machine learning to distinguish between microplastic particles and microalgae in seawater samples. Developing reliable automated methods for identifying microplastics in complex environmental samples is critical for accurate contamination monitoring.
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.
Hyperspectral imaging as an emerging tool to analyze microplastics: A systematic review and recommendations for future development
This systematic review evaluates hyperspectral imaging as a faster, more efficient method for detecting and identifying microplastics. Better detection technology is critical for understanding how much microplastic contamination exists in our food, water, and environment, and for assessing human exposure levels.
Marine Microplastic Classification by Hyperspectral Imaging: Case Studies from the Mediterranean Sea, the Strait of Gibraltar, the Western Atlantic Ocean and the Bay of Biscay
Using advanced hyperspectral imaging across water samples from four ocean regions, researchers characterized the polymer types and physical dimensions of collected microplastics, finding polyethylene, polypropylene, polystyrene, and expanded polystyrene as the most common materials. Hyperspectral imaging can analyze many particles quickly and simultaneously capture morphological data, making it a powerful tool for large-scale environmental monitoring programs.
Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
Researchers explored the use of hyperspectral imaging technology to detect and identify different types of plastic debris on beach sand. The method can distinguish between various polymer types, supporting more efficient recycling and cleanup operations. The study demonstrates a non-contact detection approach that could help prevent further degradation of shoreline plastics into microplastics.
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.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
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.
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.
Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging
Hyperspectral imaging was developed as an innovative method for characterizing marine microplastic litter collected from diverse ocean regions including the Arctic and Mediterranean, enabling rapid spectral mapping of polymer composition across samples. The approach offers a high-throughput alternative to conventional spectroscopic methods for analyzing large numbers of environmental microplastic samples.
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.
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.
Unmanned Vehicle and Hyperspectral Imager for a More Rapid Microplastics Sampling and Analysis
Researchers tested a combination of an autonomous surface vehicle and a near-infrared hyperspectral imager to rapidly sample and identify microplastics on the Norwegian coast. Results compared favorably with standard FTIR analysis and demonstrated a repeatable method for assessing spatially variable microplastic concentrations in the marine environment.
Hyperspectral Imaging Based Method for Rapid Detection of Microplastics in the Intestinal Tracts of Fish
Researchers developed a hyperspectral imaging-based method to directly detect and identify microplastics in fish intestinal tracts without requiring tissue digestion or particle extraction, enabling faster and less reagent-intensive analysis compared to conventional Raman or FTIR 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.
Efficient microplastic identification by hyperspectral imaging: A comparative study of spatial resolutions, spectral ranges and classification models to define an optimal analytical protocol
Researchers compared different hyperspectral imaging setups to find the most efficient method for identifying common microplastics like polystyrene, polypropylene, and polyethylene. They tested various spatial resolutions, spectral ranges, and classification models, finding that a 150 micrometer resolution with near-infrared range and a linear classification model provided optimal results for particles larger than 250 micrometers. The study establishes a practical protocol for rapid, automated microplastic identification in environmental samples.
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.