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Papers
61,005 resultsShowing papers similar to Shedding light on the polymer’s identity: Microplastic detection and identification through nile red staining and multispectral imaging (FIMAP)
ClearIntelligent Visible-Near Infrared Micro-Hyperspectral Sensing System for Rapid Chemical Mapping of Microplastics and Metal Oxides
Identifying and mapping microplastics quickly and accurately is a major challenge for environmental monitoring, and this study introduces a low-cost imaging system combining visible and near-infrared light with deep-learning AI to classify different types of microplastics and other materials. The system achieved 97% accuracy in distinguishing between eight different chemical species — including spectrally similar plastics — while being far faster and cheaper than conventional methods like electron microscopy. This technology could make large-scale microplastic screening in food, water, and environmental samples much more practical.
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.
Development of a Near-Infrared Imaging System for Identifying Microplastics in Water
Researchers developed a near-infrared imaging system capable of automatically identifying and characterizing microplastics suspended in water, successfully obtaining material identification images without the manual sorting typically required by conventional methods.
Automatic microplastic classification using dual-modality spectral and image data for enhanced accuracy
A dual-modality classification system combining FTIR spectral data and microscope images achieved 99% accuracy in automatically identifying five common microplastic polymer types. The study deployed a web application (MPsSpecClassify) that enables researchers to efficiently classify microplastics, addressing the time-consuming and error-prone nature of manual spectral analysis.
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.
Multispectral Imaging as a Tool for Identifying Spectral Responses of Different Plastic Materials
Researchers applied multispectral imaging integrated with principal component analysis to identify and distinguish different plastic materials and microplastics within mixtures, extracting pure spectral end members for each plastic type from every pixel of the image. The method successfully identified randomly dispersed microplastics in water, offering a portable and non-invasive alternative to conventional plastic identification techniques.
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.
Automated identification and quantification of microfibres and microplastics
Researchers developed an automated method using FTIR imaging data analysis to simultaneously identify and quantify both microplastics and microfibers in environmental samples. Automation improves throughput and consistency compared to manual identification, addressing a key bottleneck in large-scale microplastic monitoring.
Wide-field microplastic identification based on spectrum and deep learning
Researchers developed a wide-field dispersion imaging system capable of capturing real-time spectral images at low cost and demonstrated its high accuracy for identifying microplastic materials by polymer type. The system combines spectral analysis with deep learning to enable rapid, large-area microplastic identification in environmental samples.
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.
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.
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.
Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning
Researchers developed a new method combining Raman imaging with machine learning to identify and visualize microplastics in environmental samples without destroying them. The technique can distinguish between different polymer types and map their distribution within a sample. The study offers a faster, more accurate approach to microplastic detection that could improve environmental monitoring efforts.
Polymer Sorting Through Fluorescence Spectra
Identifying which type of plastic a particle is made of is a key step in microplastics research, and this study explored using fluorescence spectroscopy as a faster, cheaper alternative to standard methods. By exposing six common polymers to different light wavelengths and analyzing their fluorescence signatures, the researchers found combinations of wavelengths that could reliably distinguish between plastics like polystyrene, polyamide, and polypropylene. This technique could streamline polymer identification in large-scale environmental monitoring programs.
Characterization of Nile Red-Stained Microplastics through Fluorescence Spectroscopy
Researchers developed an improved method for characterizing microplastics using Nile Red fluorescent staining combined with fluorescence spectroscopy. They found that different plastic polymers produce distinct fluorescent signatures when stained, enabling more reliable identification of plastic types. The technique offers a faster and more affordable alternative to traditional microplastic detection methods, which could help scale up environmental monitoring efforts.
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.
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.
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.
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.
Application of High-Resolution Near-Infrared Imaging Spectroscopy to Detect Microplastic Particles in Different Environmental Compartments
Researchers enhanced a lab-based near-infrared imaging spectroscopy setup with a microscopic lens to detect microplastic particles as small as 100 micrometers across multiple environmental sample types, significantly speeding up analysis compared to traditional methods. Faster, semi-automated detection tools are essential for scaling up environmental monitoring of microplastics, which currently requires labor-intensive laboratory work.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Leveraging deep learning for automatic recognition of microplastics (MPs) via focal plane array (FPA) micro-FT-IR imaging
Researchers developed PlasticNet, a deep learning neural network for identifying microplastics in environmental samples using infrared imaging, achieving over 95% accuracy across 11 common plastic types. The study demonstrates that this approach overcomes challenges posed by surface modifications and additives that make conventional spectral classification difficult.
An investigation on the applications of advanced Infrared Spectroscopy, Spectral Imaging and Machine Learning for Polymer Characterization, including microplastics
This study integrated advanced infrared spectroscopy, spectral imaging, chemometrics, and machine learning to identify and characterize microplastics and polymer degradation products. The combination of techniques improved both the accuracy and throughput of MP analysis compared to conventional methods.
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.