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Papers
61,005 resultsShowing papers similar to Saccharide concentration prediction from proxy-sea surface microlayer samples analyzed via ATR-FTIR spectroscopy and quantitative machine learning
ClearSaccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning
This study developed infrared spectroscopy methods combined with machine learning to predict saccharide concentrations in proxy sea surface microlayer samples. Accurate quantification of dissolved organics in the ocean surface layer is critical for understanding their role in cloud nucleation, ice formation, and other climatological processes.
Saccharide 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.
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.
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.
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.
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.
Machine learning-driven microplastics identification using ensemble stacking with Extra Tree meta-models from FTIR data
Researchers applied ensemble stacking machine learning to ATR-FTIR spectra for microplastic identification, finding that combining multiple classifier outputs improved polymer classification accuracy beyond any single model, particularly for weathered plastics with degraded spectral signatures.
Application of MATLAB and SAS Viya AI Models towards the Elucidation of Potential Microplastics in the Neuse River Basin
Researchers collected 18 water samples from the Neuse River basin and applied ATR-FTIR spectroscopy combined with principal component analysis, K-means clustering, MATLAB Classification Learner, and SAS Viya machine learning models to identify weathered microplastic particles whose spectra are obscured by environmental degradation. The multi-model approach improved identification accuracy by comparing against both a 7-polymer and a 9-polymer reference library.
Comparison of Different Vibrational Spectroscopic Probes (ATR-FTIR, O-PTIR, Micro-Raman, and AFM-IR) of Lipids and Other Compounds Found in Environmental Samples: Case Study of Substrate-Deposited Sea Spray Aerosols
Researchers compared four different vibrational spectroscopy techniques for analyzing lipids and other compounds in environmental samples, including sea spray aerosols. They found that infrared-based methods could clearly distinguish between different lipid structures, while Raman spectroscopy had difficulty differentiating them. The study demonstrates how combining complementary spectroscopic approaches provides the most comprehensive chemical characterization of environmental particles.
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.
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.
Optimizing spectral classification and oxidation estimation of environmental Microplastics
Researchers conducted an intercalibration exercise using 200 FTIR spectra from sea surface floating microplastics analyzed on two different spectrometers, comparing spectral preprocessing methods and library-matching tools to assess identification reliability for weathered environmental particles. They also calculated the carbonyl index for 2,000 spectra from marine floating microplastics across multiple polymer types, finding high variability in oxidation levels that complicates comparisons with accelerated aging experiments.
Optimized recognition of microplastic ATR-FTIR spectra with deep learning
Researchers developed an optimized deep learning method for identifying microplastics from ATR-FTIR spectra, improving classification accuracy for weathered and environmentally contaminated MP samples that challenge standard spectral library matching approaches.
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.
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.
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.
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.
Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models
Researchers developed a machine learning model that uses images of FTIR spectra to automatically identify chemical functional groups in unknown substances. This approach could speed up the identification of microplastic polymer types in environmental samples, making large-scale monitoring more efficient.
Automated Machine-Learning-DrivenAnalysis of Microplasticsby TGA-FTIR for Enhanced Identification and Quantification
Researchers developed an automated machine-learning-driven analysis pipeline for characterizing microplastics using thermogravimetric analysis coupled with FTIR, achieving rapid polymer identification and quantification that could enable high-throughput environmental monitoring.
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.
Detecting small microplastics down to 1.3 μm using large area ATR-FTIR
Researchers introduced large-area ATR-FTIR spectroscopy as a new technique capable of detecting microplastics as small as 1.3 micrometers, outperforming conventional micro-FTIR for small particle detection in marine water samples.
Development of a machine‐learning model for microplastic analysis in an FT‐IR microscopy image
Researchers developed a machine-learning model using a 1D convolutional neural network to classify FT-IR microscopy spectra of microplastics into 16 polymer types. The model addresses inaccuracies caused by secondary materials on real environmental samples, improving the speed and reliability of automated microplastic identification.
A Comparative Study of Machine Learning and Deep Learning Models for Microplastic Classification using FTIR Spectra
Researchers compared machine learning and deep learning models for classifying microplastics using FTIR spectra, evaluating multiple algorithmic approaches against standardised spectral datasets. The study assessed classification accuracy and computational efficiency, identifying which model architectures best discriminate between polymer types in environmental microplastic samples.