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Papers
61,005 resultsShowing papers similar to Enhanced Identification of Weathered Plastics Through the Improvement of Infrared Spectral Libraries
ClearOptimizing spectral classification and oxidation estimation of environmental Microplastics
Researchers performed an intercalibration exercise to optimize spectral classification and oxidation estimation methods for weathered environmental microplastics, finding that spectral libraries built from weathered particles improve polymer type identification accuracy compared to libraries based on pristine reference materials.
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.
Application of a Hybrid Fusion Classification Process for Identification of Microplastics Based on Fourier Transform Infrared Spectroscopy
A hybrid machine learning approach was developed to improve the identification of microplastics using infrared spectroscopy, overcoming the limitations of standard library-matching methods. The new method better handles weathered or contaminated microplastic particles, which are harder to identify using conventional approaches.
μ-FTIR Reflectance Spectroscopy Coupled with Multivariate Analysis: A Rapid and Robust Method for Identifying the Extent of Photodegradation on Microplastics
Researchers developed a faster, more sensitive method for identifying weathered microplastics using infrared reflectance spectroscopy combined with statistical analysis. The technique can classify different plastic types and assess their level of sun damage without complex data preprocessing. The approach could improve the speed and accuracy of environmental microplastic monitoring, particularly for particles that have been altered by exposure to sunlight.
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.
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.
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.
Unveiling microplastic spectral signatures under weathering and digestive environments through shortwave infrared hyperspectral sensing
Weather exposure and digestive processes significantly alter the chemical structure of microplastics — creating new carbonyl and vinyl groups detectable by shortwave infrared hyperspectral sensing — which complicates their identification in environmental samples. This study built a comprehensive spectral database of weathered and digestion-degraded plastics and showed that hyperspectral sensing can still correctly identify roughly 80% of these altered particles, offering a fast, large-area screening tool that could improve environmental microplastic monitoring.
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.
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.
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.
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.
Short-wave infrared hyperspectral imaging of microplastics: Effects of chemical and physical processes on spectral signatures and detection capabilities
Researchers evaluated short-wave infrared hyperspectral imaging for rapid microplastic detection and polymer identification, testing the effects of various physical and chemical weathering agents on spectral signatures and finding the technique effective for identifying multiple polymer types in complex samples.
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.
A New Chemometric Approach for Automatic Identification of Microplastics from Environmental Compartments Based on FT-IR Spectroscopy
Researchers developed a new chemometric approach for automatic identification of microplastics from environmental samples, designed to handle the challenges of biofilm contamination and surface aging that typically impede standard spectroscopic characterisation methods.
Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy
Researchers developed a robust automated method for identifying microplastics in environmental samples using FTIR microscopy combined with machine learning-based spectral matching, improving the consistency and efficiency of microplastic identification compared to manual evaluation.
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.
Optimizing 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.
SpectraNet: A unified deep learning framework for infrared spectroscopy-based prediction of plastic recyclability, type classification, and microplastic identification
Researchers built SpectraNet, a deep learning framework using mid-infrared spectroscopy to perform three tasks—plastic recyclability assessment, polymer type classification, and microplastic identification—supported by an open-access infrared spectral database of plastics and microplastics.
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.
Fourier-Transform Infrared Spectroscopy of Environmentally Weathered Textile Fabrics for Enhanced Microplastic Identification
This study used infrared spectroscopy to identify microplastic fibers from clothing that had been weathered by ocean conditions, finding that environmental aging makes spectral identification more difficult. Accurate detection of these aged fibers is essential for understanding the true scale of textile microplastic pollution in the ocean.
Microplastic particles in the Arctic marine environment: database of IR spectra and its analysis by machine learning methods
Researchers compiled a database of infrared spectra from microplastic particles collected in the Arctic marine environment and applied machine learning methods to automate polymer identification, addressing the labor-intensive nature of manual spectral analysis. They developed and evaluated ML classification models using real environmental polymer spectra to improve the speed and scalability of microplastic chemical characterization in polar research.
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.
Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification
This study developed a deep learning model using convolutional neural networks to automatically identify aged microplastics from their infrared spectra. Aging changes the chemical signature of plastics, making them harder to identify with conventional spectral databases. The AI approach achieved high accuracy and could significantly speed up the analysis of environmental samples where weathered microplastics are the norm.