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Papers
20 resultsShowing papers similar to An investigation on the applications of advanced Infrared Spectroscopy, Spectral Imaging and Machine Learning for Polymer Characterization, including microplastics
ClearClassifying 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.
A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics
Researchers developed a near-infrared hyperspectral imaging method combined with chemometric analysis for rapid, high-throughput identification of microplastic types in mixed samples, achieving high classification accuracy and offering a faster alternative to FTIR and Raman methods for routine monitoring.
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.
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.
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.
Comparative Study of Chemometric Approaches and Machine Learning for Miniaturized Near-infrared (micronir) Spectroscopy in Plasticwaste Sorting
This study tested a miniaturized near-infrared (NIR) spectroscopy device combined with chemometric and machine learning methods to sort different types of plastic waste. The approach accurately identified polymer types, supporting more efficient plastic recycling operations that could reduce microplastic generation.
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.
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.
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.
Contributions of Fourier transform infrared spectroscopy in microplastic pollution research: A review
This review covers advances in Fourier transform infrared (FTIR) spectroscopy techniques — including chemical imaging — for identifying polymer types in microplastic samples and tracing their fate in different environmental matrices.
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.
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.
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.
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.
Investigation of new analysis methods for simultaneous and rapid identification of five different microplastics using ATR-FTIR spectroscopy and chemometrics
Researchers developed and evaluated ATR-FTIR spectroscopy combined with chemometric analysis for simultaneous rapid identification of five common microplastic polymer types in water samples. The method achieved high classification accuracy across polymer types, offering a faster and more automated alternative to conventional single-polymer identification approaches.
Microplastic particles in the Arctic marine environment: database of IR spectra and its analysis by machine learning methods
Researchers built a database of IR spectra from microplastic particles collected across Arctic marine environments and applied machine learning methods to enable faster and less labor-intensive chemical composition analysis, identifying polymer types from spectral signatures at broad regional scales.
A Hybrid MIR-spectrum Processing Algorithm for Microplastics Analysis
Researchers developed a hybrid algorithm for classifying microplastics using their mid-infrared spectral signatures, targeting polypropylene, polyethylene, and polystyrene. The model combines principal component analysis with machine learning techniques to improve classification accuracy. The study offers an automated approach that could make routine microplastic identification faster and more reliable for environmental monitoring.
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.
Refined Analysis of Microplastics: Integrating Infrared and Raman Spectroscopy
This study optimized the use of Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy for characterizing microplastics in aquatic environments, finding that integrating both techniques improves identification accuracy and physicochemical characterization.