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61,005 resultsShowing papers similar to Rapid chemical screening of microplastics and nanoplastics by thermal desorption and pyrolysis mass spectrometry with unsupervised fuzzy clustering
ClearRapid chemical screening of microplastics and nanoplastics by thermal desorption and pyrolysis mass spectrometry with unsupervised fuzzy clustering
This study developed a thermal desorption and pyrolysis mass spectrometry method with unsupervised fuzzy clustering for rapid chemical screening of microplastics and nanoplastics. This analytical approach can identify plastic types and associated chemical contaminants simultaneously, accelerating environmental monitoring of plastic pollution.
Rapid Chemical Screening of Microplastics and Nanoplastics by Thermal Desorption and Pyrolysis Mass Spectrometry with Unsupervised Fuzzy Clustering
Researchers developed a rapid chemical screening method for microplastics and nanoplastics using thermal desorption and pyrolysis mass spectrometry combined with unsupervised fuzzy clustering, enabling polymer identification without requiring manual spectral matching. The method addresses the challenge of characterizing physically and chemically variable plastic particles in environmental samples.
Fast identification of microplastics in complex environmental samples by a thermal degradation method
Researchers developed a fast identification method for microplastics in complex environmental samples using thermal analysis, offering a high-throughput alternative to spectroscopic techniques for polymer identification.
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 methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers
Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
Automated Machine-Learning-DrivenAnalysis of Microplasticsby TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning-driven analysis pipeline for characterizing microplastics using thermogravimetric analysis coupled with FTIR, achieving rapid polymer identification and quantification that could enable high-throughput environmental monitoring.
Rapid and efficient method for assessing nanoplastics by an electromagnetic heating pyrolysis mass spectrometry
Researchers developed an electromagnetic heating pyrolysis mass spectrometry method for rapid nanoplastic characterization, demonstrating fast polymer identification and quantification at low concentrations in complex environmental samples compared to conventional thermal analysis.
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.
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.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual 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.
Detection of Nanoplastics Within Complex Environmental and Food Resources Matrices Via Machine Learning
This review examines methods for detecting nanoplastics within complex environmental and food matrices, evaluating emerging analytical approaches including single-particle ICP-MS, pyrolysis-GC/MS, and hyperspectral imaging for nanoscale plastic characterization.
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
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.
Rapid identification of marine microplastics by laser-induced fluorescence technique based on PCA combined with SVM and KNN algorithm
Researchers developed a laser-based fluorescence method combined with machine learning algorithms to rapidly identify different types of marine microplastics. The system achieved classification accuracy above 97 percent for four common plastic types at various concentrations. The technique offers a fast, non-destructive alternative to traditional laboratory methods for monitoring microplastic pollution in ocean environments.
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.
Rapid Monitoring Approach for Microplastics Using Portable Pyrolysis-Mass Spectrometry
Researchers developed a rapid monitoring method for microplastics using a portable pyrolysis-mass spectrometry system that can identify polymer types and quantify particles smaller than 5 mm in the field without lengthy laboratory preparation. The approach offers a promising tool for fast, on-site microplastic surveillance in environmental samples.
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.
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.
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.
Development of a rapid detection protocol for microplastics using reflectance-FTIR spectroscopic imaging and multivariate classification
Reflectance-FTIR spectroscopy was evaluated as a faster and more automated detection method for microplastics in environmental samples, with results showing strong potential for high-throughput screening. The method could reduce the time and cost of routine microplastic monitoring programs.
Rapid fingerprinting of source and environmental microplastics using direct analysis in real time-high resolution mass spectrometry
Researchers developed a rapid fingerprinting method using differential mobility spectrometry to identify the chemical composition and potential sources of environmental microplastics. This non-destructive approach could help identify pollution sources and inform targeted cleanup strategies.