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Papers
61,005 resultsShowing papers similar to Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion
ClearA new miniaturised short-wave infrared (SWIR) spectrometer for on-site cultural heritage investigations
Researchers developed a miniaturized short-wave infrared spectrometer prototype and validated it for on-site cultural heritage analysis, demonstrating it could differentiate historic film substrates (cellulose nitrate, acetate, and PET), screen archaeological bone collagen content, and distinguish corrosion products on bronze sculptures.
Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain
Researchers reviewed how combining machine learning with hyperspectral imaging (HSI) enables rapid, non-destructive assessment of food quality across the supply chain—from sorting and packaging to storage and sale—offering a more efficient alternative to traditional chemical testing methods.
Hyperspectral detection of soil microplastics via multimodal feature fusion and a dual-path attention residual convolutional network
A hyperspectral imaging approach combined with multimodal deep learning was developed to detect microplastics in soil, achieving high accuracy in identifying plastic particles against complex soil backgrounds. The method offers a faster, less destructive alternative to traditional chemical extraction and spectroscopy for soil monitoring.
Multiscale Dense Cross-Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification
Researchers developed a multiscale dense cross-attention mechanism with covariance pooling for hyperspectral image scene classification, addressing challenges of high dimensionality and feature redundancy in deep convolutional frameworks to improve classification accuracy.
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.
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.
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.
Spectroscopic Techniques for Identifying Pigments in Polychrome Cultural Relics
This paper is not relevant to microplastics research; it reviews non-destructive spectroscopic techniques for identifying pigments in historical cultural artifacts, with no connection to plastic pollution or environmental contamination.
Deep Learning-BasedShape Classification for Hyperspectral-ImagedMicroplastics
Researchers tested nine deep learning architectures for automating shape classification of microplastic particles in hyperspectral images, comparing performance across original and augmented datasets. The best-performing architectures achieved high accuracy, offering a faster and more consistent alternative to manual expert classification.
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.
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.
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.
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.
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.
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.
PlasticNet: Deep Learning for Automatic Microplastic Recognition via FT-IR Spectroscopy
Researchers developed PlasticNet, a deep learning algorithm that automatically identifies microplastic types from infrared spectral data, outperforming conventional library matching approaches. Automating microplastic identification could dramatically speed up the analysis of environmental samples and reduce human error.
Deep Learning for Reconstructing Low-Quality FTIR and Raman Spectra─A Case Study in Microplastic Analyses
Researchers developed a deep learning method to reconstruct low-quality FTIR and Raman spectra, demonstrating its effectiveness for automated microplastic analysis where rapid measurement workflows produce noisy, challenging spectral datasets.
Cascaded Improved Neural Network for the Reconstruction, Classification, and Unmixing of the Raman Spectra of Mixed Microplastics.
Researchers developed a cascaded neural network combining reconstruction, classification, and spectral unmixing to analyze mixed microplastic Raman spectra, achieving improved identification accuracy under complex environmental conditions where traditional preprocessing algorithms struggle with overlapping spectral peaks.
Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms
Researchers developed a deep learning approach combined with image sensors to improve plastic waste classification and recycling efficiency. The study demonstrates that this method can distinguish between chemically similar plastics like PET and PET-G that conventional near-infrared spectroscopy struggles to differentiate, potentially improving automated sorting systems.
The contamination of in situ archaeological remains: A pilot analysis of microplastics in sediment samples using μFTIR
Researchers presented what is believed to be the first evidence of microplastic contamination in archaeological sediment samples, using micro-FTIR spectroscopy to identify polymer types and size ranges. The study suggests that microplastics may migrate through archaeological layers over time, potentially compromising the scientific integrity of archaeological deposits and the environmental data they contain.
Recognition of microplastic aging features based on multimodal data fusion and attention mechanisms
Researchers developed a deep learning model integrating SEM images and FT-IR spectral data via multimodal fusion and attention mechanisms to recognize aging features in 1,371 microplastic samples across seven aging types, achieving 96.4% validation accuracy compared to 85.3% for image-only and 47.8% for spectroscopy-only models.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
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.
Tracing Microplastic Aging Processes Using Multimodal Deep Learning: A Predictive Model for Enhanced Traceability
Researchers developed a multimodal deep learning model that combines surface imaging and infrared spectroscopy data to trace the aging history of microplastics. The model achieved 93% accuracy in predicting the major aging factors that weathered the particles, outperforming single-data approaches by 5 to 20%. When applied to naturally aged microplastics from real environments, the predictions aligned with known environmental conditions, offering a new tool for environmental risk assessment.