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61,005 resultsShowing papers similar to Hyperspectral imaging: An early systematic review of emerging applications for rapid microplastic analysis
ClearHyperspectral 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.
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.
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.
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.
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.
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.
Hyperspectral Imaging Algorithms and Applications: A Review
This paper is not about microplastics. It is a broad review of hyperspectral imaging algorithms and their applications across agriculture, healthcare, earth sciences, industrial manufacturing, and security, tracing development from early image processing through modern deep learning approaches. While hyperspectral imaging can be applied to microplastic detection, this review covers the technology's full range of applications rather than focusing on environmental contamination.
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.
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.
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 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.
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.
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.
Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil
Researchers evaluated whether hyperspectral imaging technology can reliably detect and quantify microplastics in soil under varying real-world conditions. They found that near-infrared imaging generally works well but is significantly affected by factors like soil moisture, microplastic color, and particle size. The study recommends sorting microplastics by size before analysis and further research into moisture effects, providing the first comprehensive evaluation of this emerging detection technology for soil monitoring.
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.
Microscopic Hyperspectral Image Analysis via Deep Learning
This paper reviews deep learning approaches applied to microscopic hyperspectral imaging, a technique that captures detailed spectral data useful for identifying materials including microplastics. Advances in portable cameras and AI analysis are expanding applications for environmental monitoring and pollution detection.
Recent Trends in Microplastic Detection based on Machine Learning and Artificial Intelligence
This chapter reviews recent trends in using machine learning and artificial intelligence for microplastic detection, addressing limitations of traditional microscopic and spectroscopic methods. The authors highlight how hyperspectral imaging combined with ML algorithms can classify and quantify microplastic samples more effectively, with improved recognition speed and cost-efficiency. The study suggests that AI-based approaches have significant potential for advancing large-scale microplastic monitoring.
Optimization of a hyperspectral imaging system for rapid detection of microplastics down to 100 µm
Researchers optimised a commercially available hyperspectral near-infrared imaging system with symmetrical converged-light lamps and macro-photography optics to enable rapid detection of microplastics down to 100 µm, substantially expanding the size range detectable by hyperspectral methods without requiring lengthy sample preparation.
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.
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.
Hyperspectral Imaging Algorithms and Applications: A Review
This paper is not about microplastics; it is a comprehensive review of hyperspectral imaging algorithms and applications across agriculture, food safety, healthcare, earth sciences, and manufacturing, covering algorithmic development from classical image processing to deep learning.
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.
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 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.