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Papers
61,005 resultsShowing papers similar to Reducing SpectralConfusion in Microplastic Analysis:A U‑Net Deep Learning Approach
ClearReducing Spectral Confusion in Microplastic Analysis: A U-Net Deep Learning Approach
A common problem in microplastic detection using Raman spectroscopy is that fatty acids in environmental samples look chemically similar to polyethylene (a common plastic), causing misidentification. This study trained a deep learning model (U-Net architecture) to distinguish polyethylene from fatty acids and other organic compounds based on subtle spectral differences, achieving accurate classification. Better detection methods are foundational to all microplastic research, and this AI-assisted approach could reduce false positives in 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.
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.
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.
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.
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.
Component identification for the SERS spectra of microplastics mixture with convolutional neural network
Researchers developed a convolutional neural network that identified microplastic components in mixed surface-enhanced Raman spectroscopy samples with 99.54% accuracy, outperforming traditional methods without requiring spectral preprocessing.
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.
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.
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.
MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams
Researchers developed MP-Net, a deep learning model based on U-Net architecture, that accurately segments and quantifies fluorescent microplastics in microscopy images of clams, achieving over 90% accuracy and enabling faster, more reliable environmental monitoring.
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 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.
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.
Detection of Microplastics in Coastal Environments Based on Semantic Segmentation
Researchers developed a deep learning semantic segmentation approach for detecting microplastics on sandy beaches at the pixel level, evaluating 12 models including U-Net variants and transformer architectures under real-world conditions.
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.
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.
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.
Rapid Identification of Plastic Beverage Bottles by Using Raman Spectroscopy Combined With Machine Learning Algorithm
Researchers collected 40 commercial plastic beverage bottles, recorded their Raman spectra, and used a convolutional neural network to classify them into PET, PE, and three PET subcategories. Spectral preprocessing combined with the CNN model enabled rapid and accurate identification of bottle polymer types, demonstrating the potential for Raman spectroscopy with machine learning in forensic and environmental plastic characterization.
Application of a modified set of GoogLeNet and ResNet-18 convolutional neural networks towards the identification of environmentally derived-MPLs in the Yadkin-pee dee river basin
Transfer learning applied to GoogLeNet and ResNet-18 convolutional neural networks achieved over 90% accuracy in identifying environmentally derived microplastics from Raman spectroscopy images collected in the Yadkin-Pee Dee River Basin.