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Papers
61,005 resultsShowing papers similar to Rapid Identification of Beached Marine Plastics Pellets Using Laser-Induced Breakdown Spectroscopy: A Promising Tool for the Quantification of Coastal Pollution
ClearIdentifying 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.
Identification of 20 polymer types by means of laser-induced breakdown spectroscopy (LIBS) and chemometrics
Researchers developed a laser-based identification technique that can distinguish among 20 different types of plastic using chemical analysis and machine learning, even in colored or additive-containing samples — a higher number than any previously published method. Rapid and reliable plastic identification is a critical step for improving plastic waste sorting and understanding the composition of environmental microplastic pollution.
Identification and Quantification of Microplastics in the Marine Environment Using the Laser Direct Infrared (LDIR) Technique
Researchers evaluated the laser direct infrared (LDIR) technique for identifying and quantifying marine microplastics, demonstrating it as a faster and more automated alternative to conventional FTIR methods with comparable accuracy.
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.
Identification of marine microplastics by laser-induced fluorescence spectroscopy: 1-Dimensional convolutional neural network and continuous convolutional model
Researchers investigated using laser-induced fluorescence spectroscopy combined with deep learning models to identify six types of marine microplastics. A continuous convolution neural network model achieved 99.5% classification accuracy, outperforming a standard 1D convolutional network at 97.5%. The approach offers a faster and less expensive alternative to traditional FTIR and Raman spectroscopy methods for microplastic identification.
Laser-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.
Quantification and characterization of microplastics in coastal environments: Insights from laser direct infrared imaging
Researchers used laser direct infrared imaging to identify and quantify microplastics in sediment and seawater samples from coastal areas in Auckland, New Zealand. The study detected nine common plastic polymer types and demonstrated that this analytical technique provides efficient and accurate characterization of microplastic contamination in environmental samples.
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.
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.
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.
Study on Rapid Recognition of Marine Microplastics Based on Raman Spectroscopy
Researchers developed a rapid identification system for marine microplastics using Raman spectroscopy, enabling quick determination of plastic type and size. Fast, accurate identification tools are critical for monitoring the growing problem of microplastic pollution in ocean environments.
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 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.
Instant plastic waste detection on shores using laser-induced fluorescence and associated hyperspectral imaging
Researchers demonstrated the use of laser-induced fluorescence combined with hyperspectral imaging for rapid detection of plastic waste on shorelines. The study suggests this technology could enable efficient, real-time monitoring of plastic pollution on beaches and coastal areas through remote sensing approaches.
Polymer Type Identification of Marine Plastic Litter Using a Miniature Near-Infrared Spectrometer (MicroNIR)
Researchers tested a miniature near-infrared spectrometer (MicroNIR) for rapidly identifying polymer types in marine plastic litter collected from beaches, finding it could accurately distinguish common plastics like polyethylene and polypropylene. Low-cost, portable identification tools are important for large-scale monitoring of marine plastic pollution.
Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery
Researchers used a laboratory spectrometer to measure the light reflectance of common plastic types found in the Mediterranean Sea as a step toward developing remote sensing methods to detect marine plastic pollution from satellites or aircraft. Aerial monitoring of plastic pollution could revolutionize our ability to track and manage large-scale ocean plastic contamination.
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.
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.
Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance
Researchers compared machine learning models to predict concentrations of LDPE, PET, and ABS microplastics in beach sediments using visible-near-infrared spectral reflectance, demonstrating that spectroscopic methods can efficiently estimate microplastic pollution in understudied terrestrial and coastal environments.
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.
Spatial distribution of microplastics in the tropical Indian Ocean based on laser direct infrared imaging and microwave-assisted matrix digestion
Researchers characterized microplastic distribution across the tropical Indian Ocean using a new quantum cascade laser imaging method, finding an average concentration of 50 particles per cubic meter at depths of 6 meters. The new analytical approach analyzed up to 1,000 particles per hour with over 97% identification accuracy, enabling faster and more reliable monitoring.
Harnessing Machine Learning and Deep Learning Approaches for Laser‐Induced Breakdown Spectroscopy Data Analysis: A Comprehensive Review
Machine learning and deep learning approaches were reviewed for their applications in detecting, classifying, and quantifying microplastics in environmental and biological samples. The review highlights how AI is transforming the speed and scale of microplastic analysis, enabling large-scale monitoring programs.
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.
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.