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61,005 resultsShowing papers similar to Optimizing spectral classification and oxidation estimation of environmental Microplastics
ClearOptimizing spectral classification and oxidation estimation of environmental Microplastics
Researchers conducted an intercalibration exercise using 200 FTIR spectra from sea surface floating microplastics analyzed on two different spectrometers, comparing spectral preprocessing methods and library-matching tools to assess identification reliability for weathered environmental particles. They also calculated the carbonyl index for 2,000 spectra from marine floating microplastics across multiple polymer types, finding high variability in oxidation levels that complicates comparisons with accelerated aging experiments.
Enhanced Identification of Weathered Plastics Through the Improvement of Infrared Spectral Libraries
Researchers developed an improved infrared spectral library specifically designed to identify weathered and degraded plastics that conventional libraries often misidentify. The new library increased match rates by 7.3% for thermally oxidized plastics and improved identification of mechanically abraded samples, addressing a significant gap in accurate microplastic detection and environmental risk assessment.
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.
Impact of weathering on the chemical identification of microplastics from usual packaging polymers in the marine environment
The impact of environmental weathering on the chemical identification of common microplastics was investigated, examining how UV radiation, mechanical abrasion, and microbial activity alter the spectroscopic signatures used for polymer identification. Weathered plastics were harder to correctly identify than pristine ones, highlighting the need for reference libraries that include aged material.
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
Machine learning models were applied to Raman spectroscopy data to improve polymer type identification in environmentally weathered microplastics, which are harder to classify than pristine samples. The approach achieved better accuracy by accounting for spectral changes caused by UV exposure and physical degradation.
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.
Optimized recognition of microplastic ATR-FTIR spectra with deep learning
Researchers developed an optimized deep learning method for identifying microplastics from ATR-FTIR spectra, improving classification accuracy for weathered and environmentally contaminated MP samples that challenge standard spectral library matching approaches.
Polymer weathering under simulated solar radiation and comparison to stormwater and estuarine microplastics
Researchers weathered polyethylene and polypropylene plastics under simulated sunlight in water for 90 days and compared their spectral changes to those found in environmental microplastics from stormwater and estuaries. They found that polypropylene degraded faster than polyethylene and that spectral databases had difficulty accurately identifying heavily weathered plastics. The study highlights challenges in identifying and age-dating microplastics found in the environment.
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
Researchers applied machine learning to Raman spectroscopy data to classify microplastic polymer types, finding the approach particularly valuable for identifying environmentally weathered particles that are harder to analyze with standard methods. Machine learning tools could improve the speed and accuracy of microplastic identification in environmental monitoring.
Optimizing microplastic analysis through comparative FTIR and raman spectroscopy: Addressing challenges in environmental degradation studies
This study optimized microplastic analysis by comparing FTIR and Raman spectroscopy approaches for identifying degraded polymer particles in environmental samples where photooxidation and mechanical fragmentation have altered spectral signatures. A combined spectroscopy approach outperformed either technique alone for accurately identifying degraded microplastics in complex environmental matrices.
μATR-FTIR Spectral Libraries of Plastic Particles (FLOPP and FLOPP-e) for the Analysis of Microplastics
Researchers developed two novel FTIR spectral libraries (FLOPP and FLOPP-e) specific to microplastic particles, including weathered samples, demonstrating improved spectral matching accuracy for identifying environmental microplastics compared to conventional polymer databases.
Optimizing microplastic analysis through comparative FTIR and raman spectroscopy: Addressing challenges in environmental degradation studies
Researchers compared FTIR and Raman spectroscopy for analyzing degraded microplastic polymers in environmental samples, evaluating how polymer aging affects identification accuracy. The study found that spectral databases based on pristine polymers can misidentify weathered microplastics, calling for updated reference libraries.
A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples
Researchers compared microscopic and spectroscopic methods for analyzing microplastics in environmental samples, evaluating accuracy and efficiency and finding that spectroscopic confirmation substantially reduces misidentification errors.
Machine learning-driven microplastics identification using ensemble stacking with Extra Tree meta-models from FTIR data
Researchers applied ensemble stacking machine learning to ATR-FTIR spectra for microplastic identification, finding that combining multiple classifier outputs improved polymer classification accuracy beyond any single model, particularly for weathered plastics with degraded spectral signatures.
Spectral analysis of environmental microplastic polyethylene (PE) using average spectra
Researchers analyzed the spectral properties of polyethylene microplastics collected from Tokyo Bay's surface waters. The study found that the shape, color, and weathering history of microplastic particles all affect their spectral signatures, and suggests that using oxidized polyethylene as a reference standard may improve the accuracy of identifying environmental microplastics.
Microplastic Spectral Classification Using Deep Learning with Denoising and Dimensionality Reduction
Researchers developed a deep learning approach for microplastic spectral classification that incorporates denoising and dimensionality reduction steps, improving the accuracy of identifying and classifying microplastic polymer types from spectral data in marine ecosystems.
MPX_specDB: A FAIR spectroscopic data collection for enhanced detection of weathered and biofouled polymers
Researchers developed MPX_specDB, a FAIR spectroscopic database for microplastic analysis that includes spectra of weathered polymers alongside pristine materials, addressing the underestimation of weathered plastics when pristine-only libraries are used for identification. The database supports more accurate polymer identification in environmental samples where weathering has altered spectral signatures.
Development of representative convolutional neural network based models for microplastic spectral identification
Researchers developed more representative convolutional neural network (CNN) models for microplastic spectral identification by training on expanded spectral databases that include greater diversity of plastic types, aging stages, secondary additives, pigments, and environmental contamination, outperforming library-search methods in classification accuracy and speed.
Degradation degree analysis of environmental microplastics by micro FT-IR imaging technology
Researchers used micro-FTIR spectral-image fusion to classify the degradation degree of polyethylene microplastics collected from coastal environments, achieving 97.1% classification accuracy and enabling estimation of environmental persistence time from spectral data.
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.
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.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
MPX_specDB: A FAIR spectroscopic data collection for enhanced detection of weathered and biofouled polymers
Researchers developed MPX_specDB, a FAIR-compliant spectroscopic database for weathered microplastics, designed to improve the identification accuracy of environmentally realistic, aged plastic particles. The database addresses a key limitation in current spectral libraries that rely predominantly on pristine polymer reference spectra.
Weathering-independent differentiation of microplastic polymers by reflectance IR spectrometry and pattern recognition
Researchers developed a weathering-independent method for identifying microplastic polymer types using reflectance infrared spectrometry combined with pattern recognition techniques including principal components analysis and classification trees, demonstrating reliable polymer differentiation even when field samples are weathered or biofouled.