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Papers
61,005 resultsShowing papers similar to Classification of household microplastics using a multi-model approach based on Raman spectroscopy
ClearCharacterization 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 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.
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.
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.
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.
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
Machine learning models were applied to Raman spectroscopy data to improve polymer type identification in environmentally weathered microplastics, which are harder to classify than pristine samples. The approach achieved better accuracy by accounting for spectral changes caused by UV exposure and physical degradation.
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.
A Study of High-Frequency Noise for Microplastics Classification Using Raman Spectroscopy and Machine Learning
This study developed a machine learning approach to classify microplastics using Raman spectroscopy data with high-frequency noise, demonstrating that noise-robust models can accurately identify plastic polymer types for environmental monitoring applications.
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.
Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning
Researchers combined highly reflective substrates with machine learning to accurately identify individual nanoplastic particles using Raman spectroscopy, a technique that traditionally struggles with particles this small. Their approach achieved over 97 percent accuracy in distinguishing between different types of nanoplastics including polystyrene, polymethyl methacrylate, and polyethylene. The method represents a significant advance in the ability to detect and monitor nanoplastic pollution at the individual particle level.
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.
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.
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.
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.
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.
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.
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.
Identification of microplastics using Raman spectroscopy: Latest developments and future prospects
This review summarizes the latest advances in using Raman spectroscopy to identify microplastics in environmental samples, highlighting improvements in speed, sensitivity, and the ability to characterize plastic type and surface chemistry.
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.
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.
Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning
Researchers developed a new method combining Raman imaging with machine learning to identify and visualize microplastics in environmental samples without destroying them. The technique can distinguish between different polymer types and map their distribution within a sample. The study offers a faster, more accurate approach to microplastic detection that could improve environmental monitoring efforts.
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.
Microplastic identification using Raman microsocpy
Researchers developed and implemented a Raman spectroscopy system for rapid detection and identification of microplastic particles on substrates. The system enables efficient chemical characterization of microplastics found across diverse environmental matrices including ocean, lakes, soil, beach sediment, and human blood.