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Papers
20 resultsShowing papers similar to Microplastic particles in the Arctic marine environment: database of IR spectra and its analysis by machine learning methods
ClearMicroplastic 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.
Classifying polymers with mid-IR spectra and machine learning: From monitoring to detection
Researchers applied machine learning to mid-infrared spectra to automatically classify different types of plastic polymers found in the environment. Accurate polymer identification is essential for microplastic research, and this automated approach could improve monitoring efficiency and data consistency across studies.
A comparison of machine learning techniques for the detection of microplastics
This German-language study compared machine learning algorithms for classifying microplastics based on their infrared spectra, finding that several methods could reliably distinguish polymer types. Automating microplastic identification through machine learning could greatly increase the speed and throughput of environmental monitoring.
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.
A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
Researchers developed a machine learning method to automatically identify the chemical composition of microplastics from FTIR spectroscopy data collected during the Tara Mediterranean expedition. The algorithm performed well for common polymers like polyethylene and was applied to classify over 4,000 unidentified microplastic spectra. The study demonstrates that automated identification tools can significantly speed up large-scale microplastic pollution surveys while maintaining acceptable accuracy.
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.
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.
Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models
Researchers compared multiple machine learning and deep learning models for identifying polymer types from mid-infrared spectral data using a small reference dataset, finding that certain deep learning architectures outperformed traditional methods even with limited training examples, supporting automated microplastic identification.
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.
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.
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.
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.
Study on marine microplastics monitoring based on infrared spectroscopy technology
Researchers developed an infrared spectroscopy-based monitoring system for marine microplastics, applying support vector machine algorithms to hyperspectral images to identify plastic types and abundances in seawater. The study found microplastic abundances ranging from roughly 5 to 39 particles per litre across sampling sites, with fibers (53-68%) and debris (23-34%) as dominant shapes, demonstrating the method's feasibility for rapid environmental monitoring.
Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy
Researchers evaluated multiple machine learning algorithms for automatically classifying ocean microplastics using infrared spectroscopy data across 13 polymer types. The study found that Support Vector Machine classifiers provided the best balance of simplicity and accuracy, offering a practical tool for faster and more reliable identification of microplastic contaminants.
Spectral Classification of Large-Scale Blended (Micro)Plastics Using FT-IR Raw Spectra and Image-Based Machine Learning
Researchers developed and compared four machine learning classifiers for identifying microplastic types from Fourier transform infrared spectroscopy data using large-scale blended plastic datasets. The study found that a 1D convolutional neural network achieved the best overall accuracy at over 97%, outperforming decision tree and random forest models, offering a scalable alternative to traditional library-search methods for microplastic identification.
A Comparative Study of Machine Learning and Deep Learning Models for Microplastic Classification using FTIR Spectra
Researchers compared machine learning and deep learning models for classifying microplastics using FTIR spectra, evaluating multiple algorithmic approaches against standardised spectral datasets. The study assessed classification accuracy and computational efficiency, identifying which model architectures best discriminate between polymer types in environmental microplastic samples.
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.
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.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
An ensemble machine learning method for microplastics identification with FTIR spectrum
Researchers developed an ensemble machine learning method to automatically identify microplastics using Fourier transform infrared (FTIR) spectroscopy data. The approach combines multiple classification algorithms to improve accuracy over individual methods for detecting and categorizing microplastic particles. The study suggests this automated approach could help standardize and accelerate microplastic monitoring in marine environments.