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61,005 resultsShowing papers similar to Saccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning
ClearSaccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning
Researchers developed infrared spectroscopy combined with machine learning to analyze dissolved organic carbon in sea surface microlayer samples more efficiently. The sea surface microlayer is a critical zone where microplastics concentrate and interact with marine chemistry.
Saccharide concentration prediction from proxy-sea surface microlayer samples analyzed via ATR-FTIR spectroscopy and quantitative machine learning
Researchers investigated machine learning models for predicting saccharide concentrations in sea surface microlayer (SSML) samples analyzed by ATR-FTIR spectroscopy, evaluating support vector regression (SVR) and other approaches on proxy-SSML samples. SVR models achieved the best predictive accuracy for saccharide concentration, enabling high-throughput SSML analysis to better understand organic enrichment and climate-relevant aerosol particle release.
Microplastic particles in the Arctic marine environment: database of IR spectra and its analysis by machine learning methods
Researchers built a database of IR spectra from microplastic particles collected across Arctic marine environments and applied machine learning methods to enable faster and less labor-intensive chemical composition analysis, identifying polymer types from spectral signatures at broad regional scales.
Microplastic particles in the Arctic marine environment: database of IR spectra and its analysis by machine learning methods
Researchers compiled a database of infrared spectra from microplastic particles collected in the Arctic marine environment and applied machine learning methods to automate polymer identification, addressing the labor-intensive nature of manual spectral analysis. They developed and evaluated ML classification models using real environmental polymer spectra to improve the speed and scalability of microplastic chemical characterization in polar research.
Multi Analyte Concentration Analysis of Marine Samples Through Regression Based Machine Learning
Researchers used Raman spectroscopy combined with machine learning to identify concentrations of multiple chemical compounds in marine water samples. The study demonstrates that this approach offers a low-cost, portable method for monitoring ocean chemistry, which is relevant for understanding environmental health in marine ecosystems.
Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy
Researchers evaluated multiple machine learning algorithms for automatically classifying ocean microplastics using infrared spectroscopy data across 13 polymer types. The study found that Support Vector Machine classifiers provided the best balance of simplicity and accuracy, offering a practical tool for faster and more reliable identification of microplastic contaminants.
Study on marine microplastics monitoring based on infrared spectroscopy technology
Researchers developed an infrared spectroscopy-based monitoring system for marine microplastics, applying support vector machine algorithms to hyperspectral images to identify plastic types and abundances in seawater. The study found microplastic abundances ranging from roughly 5 to 39 particles per litre across sampling sites, with fibers (53-68%) and debris (23-34%) as dominant shapes, demonstrating the method's feasibility for rapid environmental monitoring.
Classifying polymers with mid-IR spectra and machine learning: From monitoring to detection
Researchers applied machine learning to mid-infrared spectra to automatically classify different types of plastic polymers found in the environment. Accurate polymer identification is essential for microplastic research, and this automated approach could improve monitoring efficiency and data consistency across studies.
An investigation on the applications of advanced Infrared Spectroscopy, Spectral Imaging and Machine Learning for Polymer Characterization, including microplastics
This study integrated advanced infrared spectroscopy, spectral imaging, chemometrics, and machine learning to identify and characterize microplastics and polymer degradation products. The combination of techniques improved both the accuracy and throughput of MP analysis compared to conventional methods.
Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning
Researchers found that long-term aged microplastics adsorb 7-13 times more pharmaceuticals and personal care products than pristine microplastics, and developed machine learning models using infrared spectroscopy that predicted adsorption capacity with over 96% accuracy.
A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
Researchers developed a machine learning method to automatically identify the chemical composition of microplastics from FTIR spectroscopy data collected during the Tara Mediterranean expedition. The algorithm performed well for common polymers like polyethylene and was applied to classify over 4,000 unidentified microplastic spectra. The study demonstrates that automated identification tools can significantly speed up large-scale microplastic pollution surveys while maintaining acceptable accuracy.
A New Chemometric Approach for Automatic Identification of Microplastics from Environmental Compartments Based on FT-IR Spectroscopy
Researchers developed a new chemometric approach for automatic identification of microplastics from environmental samples, designed to handle the challenges of biofilm contamination and surface aging that typically impede standard spectroscopic characterisation methods.
An ensemble machine learning method for microplastics identification with FTIR spectrum
Researchers developed an ensemble machine learning method to automatically identify microplastics using Fourier transform infrared (FTIR) spectroscopy data. The approach combines multiple classification algorithms to improve accuracy over individual methods for detecting and categorizing microplastic particles. The study suggests this automated approach could help standardize and accelerate microplastic monitoring in marine environments.
Detection of microplastics in sea salt using hyperspectral imaging and machine learning methods: Pollution control in the Mediterranean sea as a case study
Hyperspectral imaging combined with machine learning was used to detect and classify microplastics in Mediterranean sea salt samples, demonstrating a rapid, non-destructive analytical approach with potential for routine quality control in the food industry.
A3M-CIR: An Attention-Aware Adversarial Masking Based Self-Supervised Chemometric Framework for Robust Infrared Spectral Analysis
Scientists developed a new computer system called A3M-CIR that can better analyze infrared light readings to identify chemicals and materials. The system learns to recognize patterns without needing lots of labeled examples, making it more reliable when analyzing samples under different conditions or with background noise. This could improve how we detect harmful substances like microplastics in food, water, and medical samples, leading to better health monitoring and safety testing.
Microplastic fouling: A gap in knowledge and a research imperative to improve their study by infrared characterization spectroscopy
Researchers analysed 4,042 infrared spectra of polystyrene, polyethylene, and polypropylene microplastics collected from the Mediterranean Sea using principal component analysis, identifying that spectral variability in weathered marine microplastics is primarily driven by three processes: chemical ageing, organic fouling, and inorganic fouling.
Deep convolutional neural networks for aged microplastics identification by Fourier transform infrared spectra classification
This study developed a deep learning model using convolutional neural networks to automatically identify aged microplastics from their infrared spectra. Aging changes the chemical signature of plastics, making them harder to identify with conventional spectral databases. The AI approach achieved high accuracy and could significantly speed up the analysis of environmental samples where weathered microplastics are the norm.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
Identification of Polymers with a Small Data Set of Mid-infrared Spectra: A Comparison between Machine Learning and Deep Learning Models
Researchers compared multiple machine learning and deep learning models for identifying polymer types from mid-infrared spectral data using a small reference dataset, finding that certain deep learning architectures outperformed traditional methods even with limited training examples, supporting automated microplastic identification.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning
Researchers developed machine learning approaches for automated microplastic identification in environmental samples from micro-FTIR imaging data, demonstrating improved accuracy and speed compared to traditional spectral library search methods for scalable analysis.
Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance
Researchers compared machine learning models to predict concentrations of LDPE, PET, and ABS microplastics in beach sediments using visible-near-infrared spectral reflectance, demonstrating that spectroscopic methods can efficiently estimate microplastic pollution in understudied terrestrial and coastal environments.
Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning system to identify and measure microplastics using a combination of heat analysis and infrared spectroscopy. The system can distinguish between different plastic types more accurately and faster than manual methods. Better detection tools like this are important because reliable measurement of microplastics in food, water, and the environment is essential for understanding human exposure levels.