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Papers
61,005 resultsShowing papers similar to Identification of 20 polymer types by means of laser-induced breakdown spectroscopy (LIBS) and chemometrics
ClearLaser-induced breakdown spectroscopy with neural network approach for plastic identification and classification in waste management
Researchers applied laser-induced breakdown spectroscopy combined with neural network algorithms to identify and classify different plastic types, addressing the need for rapid and accurate plastic sorting in recycling chains. The system demonstrated high classification accuracy for common polymer types based on their elemental emission spectra.
Identifying microplastic litter with Laser Induced Breakdown Spectroscopy: A first approach
Researchers demonstrated that Laser Induced Breakdown Spectroscopy (LIBS) can identify microplastic particles by their spectral fingerprints, offering a first approach to a rapid analytical technique for distinguishing plastic litter types.
Rapid Identification of Beached Marine Plastics Pellets Using Laser-Induced Breakdown Spectroscopy: A Promising Tool for the Quantification of Coastal Pollution
Researchers applied laser-induced breakdown spectroscopy combined with chemometric analysis to rapidly identify and classify beached plastic pellets by polymer type, achieving over 80% accuracy and demonstrating its potential as a fast, field-deployable tool for coastal pollution monitoring.
Laser-Induced Breakdown Spectroscopy for direct analysis of pristine and environmentally aged microplastics: A PCA-based approach
Researchers combined a rapid laser analysis technique (LIBS) with statistical pattern recognition to distinguish between fresh and environmentally aged microplastics made of polystyrene, polyethylene, and PVC. They found that aging — especially biological aging with microbe growth — left distinct chemical fingerprints on particle surfaces, offering a faster way to monitor how microplastics change as they degrade in the environment.
Identifying plastics with photoluminescence spectroscopy and machine learning
Researchers showed that combining photoluminescence spectroscopy (shining light on plastic and measuring what comes back) with machine learning can reliably identify different types of plastic materials. This low-cost, widely accessible approach could help scientists track and characterize plastic pollution in the environment at a global scale.
Online in situ detection of atmospheric microplastics based on laser-induced breakdown spectroscopy
Researchers developed a laser-based detection system combined with machine learning that can identify and classify different types of microplastics in the air in real time. The system achieved high accuracy in distinguishing between common plastic types like polyethylene, polystyrene, and PVC. Better tools for monitoring airborne microplastics are important because people inhale these particles daily, and understanding what types are present in the air is the first step toward assessing respiratory health risks.
Laser-based spectroscopic techniques: A novel approach for distinguishing aging processes and types of microplastics
Researchers applied laser-based spectroscopic techniques as a novel approach to distinguish between different aging processes and plastic types in microplastic particles, addressing the challenge of identifying weathered plastics that have undergone physical and chemical degradation in the environment.
Laser-based spectroscopic techniques: A novel approach for distinguishing aging processes and types of microplastics
Researchers applied laser-based spectroscopic techniques as a novel approach to distinguish different aging processes and plastic types in microplastics, examining how biotic and abiotic degradation factors alter spectral signatures across particles ranging from 1 to 1000 microns.
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.
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.
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.
Laser-based techniques: Novel tools for the identification and characterization of aged microplastics with developed biofilm
Researchers applied laser-based analytical techniques including Raman and LIBS spectroscopy to detect and characterize microplastics covered with environmental biofilm. The methods successfully identified five polymer types under real-world conditions without requiring biofilm removal, avoiding the particle loss associated with conventional pre-treatment steps.
A Novel LIBS-Machine Learning Strategy for Multimetal Detection in Microsized PMMA Particles: Efficient Quantification for Composite Pollution
Researchers combined laser-induced breakdown spectroscopy with machine learning to simultaneously quantify three heavy metals (Cr, Pb, Cu) adsorbed to 2 µm PMMA microplastic particles, demonstrating that plastic-metal composite pollution can be characterized by optimized PLS calibration models without chemical separation.
Identification of marine microplastics based on laser-induced fluorescence and principal component analysis
Researchers developed a method to identify different types of marine microplastics using laser-induced fluorescence combined with principal component analysis. The technique successfully distinguished nine types of microplastics based on their fluorescence signatures and could detect microplastic concentrations as low as 0.03% by mass. The study suggests this approach could be a practical tool for rapid microplastic identification in marine environments.
Characterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis
Researchers developed a new method using Raman spectroscopy combined with machine learning to identify and classify seven types of microplastics with over 98% accuracy for most polymer types. The approach was also able to correctly identify real-world microplastic samples from snack boxes, water bottles, juice bottles, and medicine vials. This technique could make microplastic detection faster and more reliable compared to manual analysis methods.
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.
Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.
Researchers developed a near-infrared spectroscopy method combined with machine learning classifiers -- including PLS-DA, random forest, and XGBoost -- to rapidly identify both colored and colorless plastic fragments across different polymer types, thicknesses, and environmental backgrounds. The approach improved detection of colorless plastics that are typically underestimated in environmental surveys, with random forest achieving the highest classification accuracy.
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.
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.
Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification
Not relevant to microplastics — this study compares machine learning and chemometric methods (including PCA, SVM, and neural networks) for classifying plastic waste types using a handheld near-infrared spectrometer, focused on improving plastic recycling sorting rather than microplastic detection.
Quantification of Nanoplastics and Inorganic Nanoparticles via Laser‐Induced Breakdown Detection (LIBD)
Researchers developed a laser-induced breakdown spectroscopy (LIBS) method for quantifying nanoplastics and distinguishing them from inorganic nanoparticles in environmental samples, demonstrating detection limits and specificity suitable for routine environmental monitoring.
Integrated LIBS-Raman spectroscopic platform for concurrent elemental and molecular analysis
Researchers developed a compact platform combining laser-induced breakdown spectroscopy and Raman spectroscopy for simultaneous identification and elemental analysis of microplastic particles. The system successfully distinguished polystyrene, polyethylene, and polypropylene while detecting adsorbed lead and copper at parts-per-million levels. Machine learning classification of the Raman spectra achieved up to 99.3% accuracy, demonstrating the platform's potential for field-deployable microplastic monitoring.
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.
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.