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 In‐Situ Detection of Microplastic Particles on Food Using Hyperspectral Imaging With One‐Dimensional Convolutional Neural Network and Artificial Neural Network
ClearDetection 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.
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.
Quantitative and Qualitative Evaluation of Microplastic Contamination of Shrimp Using Visible Near-Infrared Multispectral Imaging Technology Combined with Supervised Self-Organizing Map
Scientists developed a non-destructive imaging method using visible and near-infrared multispectral technology combined with machine learning to detect and identify microplastics in shrimp. The system could distinguish between four common microplastic types (PET, PE, PP, and PS) on both minced shrimp and shrimp shell surfaces. This approach offers a faster alternative to traditional microplastic detection methods for screening seafood contamination.
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.
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.
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.
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.
A new approach to classifying polymer type of microplastics based on Faster-RCNN-FPN and spectroscopic imagery under ultraviolet light
Scientists developed an AI-based method using UV light photography to automatically identify and classify different types of microplastics, achieving 86-88% accuracy. This approach is faster and cheaper than traditional lab analysis methods that require expensive equipment. Better detection tools like this are essential for understanding how widespread microplastic contamination really is in coastal environments where people live and eat seafood.
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.
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.
Intelligent 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.
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.
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.
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.
Microplastics in food products: Prevalence, artificial intelligence based detection, and potential health impacts on humans
Researchers reviewed how microplastics enter the food supply through seafood, salt, bottled beverages, and packaging, finding that ingestion is the main human exposure route and that health risks include immune disruption, neurotoxicity, and potential cancer. The review calls for standardized detection methods, including AI-assisted imaging, and stronger regulations to reduce microplastic contamination in food.
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.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics
Researchers tested nine deep learning architectures for automating the shape classification of microplastic particles in hyperspectral images, comparing performance on original and augmented datasets. The best models achieved high classification accuracy, offering a faster and more consistent alternative to labour-intensive manual identification.
Microplastic Contamination and Detection in Food Systems: A Review of Machine Learning, Traditional Methods, and Other Relevant Factors
This review examines traditional and machine learning approaches to detecting and classifying microplastics in food systems, highlighting the limitations of FTIR, Raman spectroscopy, and SEM in complex food matrices. It identifies AI-assisted methods as promising tools for improving detection accuracy and throughput.
Microplastic Contamination and Detection in Food Systems: A Review of Machine Learning, Traditional Methods, and Other Relevant Factors
This review examines traditional and machine learning approaches to detecting and classifying microplastics in food systems, highlighting the limitations of FTIR, Raman spectroscopy, and SEM in complex food matrices. It identifies AI-assisted methods as promising tools for improving detection accuracy and throughput.
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.
Issues with the detection and classification of microplastics in marine sediments with chemical imaging and machine learning
Researchers tested near-infrared hyperspectral imaging combined with four common machine learning algorithms to detect microplastics directly in marine sediment samples, finding that the method produced a large proportion of false positives and false negatives even in simple test conditions. The results raise serious concerns about the reliability of this widely used approach for environmental microplastic monitoring.
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.