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Papers
61,005 resultsShowing papers similar to Component identification for the SERS spectra of microplastics mixture with convolutional neural network
ClearIdentification of microplastics using a convolutional neural network based on micro-Raman spectroscopy
Researchers combined micro-Raman spectroscopy with a neural network to identify microplastics, achieving over 99% accuracy across 10 different plastic types. The system was also tested on real environmental samples and performed well at classifying unknown particles. This AI-powered approach could make microplastic identification faster and more reliable for environmental monitoring.
Automatic classification of microplastics and natural organic matter mixtures using a deep learning model
Researchers developed a deep learning model using a convolutional neural network with spatial attention to classify microplastics mixed with natural organic matter from Raman spectra. The model achieved 99.54% accuracy compared to just 31.44% from conventional spectral library software, demonstrating that AI-based approaches can dramatically improve microplastic identification accuracy while reducing the need for time-intensive preprocessing steps.
Cascaded Improved Neural Network for the Reconstruction, Classification, and Unmixing of the Raman Spectra of Mixed Microplastics.
Researchers developed a cascaded neural network combining reconstruction, classification, and spectral unmixing to analyze mixed microplastic Raman spectra, achieving improved identification accuracy under complex environmental conditions where traditional preprocessing algorithms struggle with overlapping spectral peaks.
Quantitative analysis of microplastics in water environments based on Raman spectroscopy and convolutional neural network
Researchers developed a method combining Raman spectroscopy with a convolutional neural network to measure microplastic concentrations in water. The approach achieved high accuracy across six different sizes of polyethylene particles in five real-world water environments, outperforming other machine learning models and offering a practical tool for quantitative microplastic monitoring.
Detection of Microplastics in Freshwater Sediments Based on Raman Spectroscopy and Convolutional Neural Networks
Researchers developed a method combining Raman spectroscopy and convolutional neural networks to detect and classify microplastics in complex freshwater sediment samples, training the CNN on mixed spectra from extracted sediment fractions to improve detection accuracy.
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.
Development of representative convolutional neural network based models for microplastic spectral identification
Researchers developed more representative convolutional neural network (CNN) models for microplastic spectral identification by training on expanded spectral databases that include greater diversity of plastic types, aging stages, secondary additives, pigments, and environmental contamination, outperforming library-search methods in classification accuracy and speed.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification
Researchers combined a metal-based separation technique with machine learning and surface-enhanced Raman spectroscopy to detect and classify nanoplastics in environmental samples. The method achieved high accuracy in identifying different types of nanoplastics at very low concentrations. This approach could make it significantly easier and more reliable to monitor nanoplastic contamination in real-world water and soil samples.
Highly sensitive superhydrophobic SERS substrate combined with machine learning for precise identification and classification of nanoplastics
Researchers fabricated a superhydrophobic surface-enhanced Raman scattering (SERS) substrate that concentrates nanoplastics in a tiny detection zone, then combined it with machine learning to identify seven types of nanoplastics in real lake water with 99.88% accuracy, offering a practical high-throughput environmental monitoring approach.
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.
Development of a machine learning-based method for the analysis of microplastics in environmental samples using µ-Raman spectroscopy
Researchers developed a machine learning system to identify microplastics in environmental samples using Raman spectroscopy — a technique that identifies materials by how they scatter light — training it on over 64,000 spectra and achieving recall above 99% and precision above 97%. Combining the AI with human review reduced analysis time from several hours to under one hour per sample, making microplastic monitoring far more practical at scale.
Detection of Microplasticsin Freshwater SedimentsBased on Raman Spectroscopy and Convolutional Neural Networks
Researchers developed a Raman spectroscopy and convolutional neural network system for identifying and classifying microplastics in freshwater sediments, using density separation and vacuum filtration upstream and achieving improved accuracy on complex sediment matrices.
Integrating Metal Phenolic Networks-Mediated Separation and Machine Learning-Aided SERS for High-Precision Quantification and Classification of Nanoplastics
Scientists combined metal-phenolic network chemistry — which rapidly concentrates and captures nanoplastics — with machine-learning-enhanced surface-enhanced Raman spectroscopy (SERS) to accurately identify and quantify nanoplastics at very low environmental concentrations. This integrated approach addresses one of the biggest technical obstacles in nanoplastic research: detecting particles that are too small and too sparse for conventional methods to reliably find.
Investigation of multivariate analysis of surface-enhanced Raman scattering spectra using simple machine-learning models: Prediction of the composition of mixed self-assembled monolayer on gold surface
This analytical chemistry study investigates machine learning methods for analyzing surface-enhanced Raman spectroscopy (SERS) data to predict the composition of mixed chemical layers on gold surfaces. While focused on analytical chemistry, SERS is also used to identify and characterize microplastics, and improved analysis methods could benefit environmental monitoring.
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.
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.
Classification of household microplastics using a multi-model approach based on Raman spectroscopy
Researchers developed a machine learning approach combined with Raman spectroscopy to identify and classify microplastics commonly found in household products. By using multiple models together, they achieved over 98% accuracy in identifying seven types of standard and real-world microplastic samples, even after environmental weathering. This multi-model approach could provide a faster, more reliable tool for detecting and monitoring microplastic contamination in everyday settings.
Improved neural networks for the classification of microplastics via inferior quality Raman spectra
Researchers improved a neural network model to more accurately identify different types of microplastics from low-quality Raman spectroscopy readings. The enhanced model achieved nearly 98% accuracy even under poor measurement conditions with significant background noise. The study demonstrates that machine learning can make microplastic identification faster and more reliable, even when laboratory conditions are not ideal.
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.
Microplastic Spectral Classification Using Deep Learning with Denoising and Dimensionality Reduction
Researchers developed a deep learning approach for microplastic spectral classification that incorporates denoising and dimensionality reduction steps, improving the accuracy of identifying and classifying microplastic polymer types from spectral data in marine ecosystems.
Detection of Polystyrene Microplastics up to the SingleNanoparticle Limit Using SERS and Advanced ANN Design (KANformer)
Researchers developed a surface-enhanced Raman spectroscopy (SERS) platform combined with a KANformer neural network to detect polystyrene microplastics down to the single nanoparticle level, offering a highly sensitive monitoring tool for environmental plastic contamination.
Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics
This review covers how machine learning combined with Raman spectroscopy can improve the detection and identification of microplastics in environmental samples. Traditional detection methods are slow and have limitations in resolution and particle size analysis, but AI algorithms can process spectral data more quickly and accurately. Better detection tools are essential for understanding the true scale of microplastic contamination in our water, food, and environment.