We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning
ClearRaman 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi
Researchers applied deep learning to analyze light scattering patterns from mixed particles in ocean water, enabling automated identification of different particle types including sediment and biological material. This technology could be adapted to detect and classify microplastics in marine environments alongside natural particles.
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.
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.
Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring—Solutions and First Results
Researchers tested a deep UV Raman spectrometer combined with artificial intelligence for real-time detection of nitrates, selected pharmaceuticals, and common microplastic polymers in water. The system demonstrated feasibility for continuous environmental monitoring of aquatic systems without extensive sample preparation.
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.
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.
Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting
Researchers developed a non-invasive Raman spectroscopy method combined with machine learning to detect biomolecule concentrations in fermentation processes. This is an analytical chemistry and biotechnology paper with no direct connection to environmental microplastics.
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.
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 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.