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Papers
61,005 resultsShowing papers similar to Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
ClearRaman 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.
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.
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.
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.
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.
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.
Identification 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.
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.
Toward in Situ Identification of Microplastics in Water Using Raman Spectroscopy and Machine Learning
This study developed an early-stage system combining Raman spectroscopy and machine learning to identify microplastics directly in ocean water in real time, without needing to collect and process samples in a lab. A support vector machine classifier trained on spectral libraries correctly identified all pristine microplastic samples and most environmental ones, demonstrating that field-deployable automated detection is feasible. Accurate real-time monitoring tools are urgently needed to understand where microplastics concentrate in the ocean and to track pollution trends.
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.
Transformative role of deep learning in Raman spectroscopy-based detection of microplastics and nanoplastics
This review examines how deep learning is transforming the detection and classification of micro- and nanoplastics using Raman spectroscopy. Researchers found that artificial intelligence can automate spectral analysis, enabling higher-throughput and more accurate identification of plastic particles. However, most deep learning approaches have only been validated with controlled laboratory samples, and their reliability in complex environmental samples still needs improvement.
Recent Progresses in Machine Learning Assisted Raman Spectroscopy
This review covers how machine learning is being combined with Raman spectroscopy to improve the analysis of complex materials, including environmental samples. Traditional spectral analysis methods struggle with the volume and complexity of modern data, but AI techniques can extract meaningful patterns more efficiently. These advances are directly relevant to microplastic identification, where Raman spectroscopy is a primary detection tool.
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.
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.
Rapid identification of microplastic using portable Raman system and extra trees algorithm
Researchers developed a portable Raman spectroscopy system combined with a machine learning algorithm to rapidly identify and classify different types of microplastics in the field. Portable real-time identification tools are important for environmental monitoring programs that need to quickly characterize microplastics without sending samples to a laboratory.
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.
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.
Recent Advances in Raman Spectral Classification with Machine Learning
This review summarized recent advances in applying machine learning to Raman spectral classification, addressing the challenges of weak signals, complex spectra, and high-dimensional data that limit traditional chemometric methods. The advances have significant implications for automated, high-throughput microplastic polymer identification.
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.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy
Researchers combined artificial intelligence with Raman spectroscopy to rapidly detect and classify microplastic particles smaller than 10 micrometers -- a size range that is especially concerning because these tiny particles can penetrate human tissues. The AI-based method dramatically reduced the time needed to identify plastic types compared to traditional approaches, making it more practical to monitor the smallest and most potentially harmful microplastics.
Machine Learning-Enhanced Raman Spectroscopy for Microfiber Detection: From Model Development to Coastal Investigation.
Scientists developed a new method using artificial intelligence to quickly identify tiny plastic fibers in ocean water, which are the most common type of microplastic pollution. The method can accurately detect these microscopic plastic pieces in just 5 minutes, compared to much longer traditional methods. This faster detection is important because microplastics are found throughout our environment and food chain, and better monitoring could help reduce our exposure to these potentially harmful particles.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy
Researchers developed a deep learning system that can identify eight common household plastic types using Raman spectroscopy with 97% accuracy. This is faster and more reliable than traditional methods for classifying plastics. Better plastic identification tools like this are important for microplastic research because they allow scientists to quickly determine what types of plastic particles are contaminating environmental and food samples.