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20 resultsShowing papers similar to From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification
ClearFrom microplastics to pixels: Testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.
Researchers tested the robustness of two automated machine learning approaches combined with Nile red fluorescent staining for marine microplastic identification, specifically evaluating performance on environmentally weathered particles that challenge the reliability of methods developed using pristine laboratory plastics.
From microplastics to pixels: Testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.
Researchers tested the robustness of decision tree and random forest machine learning classifiers combined with Nile red fluorescent staining for automated detection and identification of microplastic polymers weathered under semi-controlled surface water and deep-sea conditions for up to one year. They found both models achieved comparable accuracy above 90% for pristine plastics, but assessed how environmental weathering affected classification reliability, also evaluating analysis time, model complexity, size detection limits, and interoperability.
Machine learning enhanced machine vision system for micro-plastics particles classification
Researchers developed a machine learning-based classification system using fluorescence microscopy with Nile Red staining to identify and categorize microplastic types in environmental samples, aiming to provide a faster and more automated alternative to labor-intensive manual identification methods.
Towards reliable data: Validation of a machine learning-based approach for microplastics analysis in marine organisms using Nile red staining
This study validated a semi-automated machine learning workflow for microplastic analysis by spiking environmentally relevant particles into samples and recovering them, addressing the high cost and variability that limits comparability across MP studies. The approach aims to improve reliability and cost-effectiveness of routine microplastic analysis.
Microplastic detection and identification by Nile red staining: Towards a semi-automated, cost- and time-effective technique
Researchers developed a semi-automated, cost-effective method for microplastic detection using Nile red fluorescent staining, showing it can significantly reduce the time and expense of identifying microplastics compared to traditional spectroscopic approaches.
Influence of intrinsic plastics characteristics on Nile Red staining and fluorescence
Researchers evaluated Nile Red fluorescent staining performance on 60 plastic particles from sandy beaches, finding that polymer type, weathering degree, and crystallinity did not significantly affect fluorescence intensity, but particle color did — with blue, green, and red particles showing lower fluorescence and white, yellow, and orange particles showing higher fluorescence. The findings suggest that plastic pigments interfere with Nile Red detection, complicating standardization of microplastic identification methods.
Rapid identification of marine microplastics by laser-induced fluorescence technique based on PCA combined with SVM and KNN algorithm
Researchers developed a laser-based fluorescence method combined with machine learning algorithms to rapidly identify different types of marine microplastics. The system achieved classification accuracy above 97 percent for four common plastic types at various concentrations. The technique offers a fast, non-destructive alternative to traditional laboratory methods for monitoring microplastic pollution in ocean environments.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Exploring Nile Red staining as an analytical tool for surface-oxidized microplastics
Scientists evaluated Nile Red, a fluorescent dye commonly used to detect microplastics, and found it works differently depending on whether microplastics have been weathered by the environment. Surface oxidation from aging in the environment changes how well the dye sticks to plastics, which means current detection methods may be undercounting weathered microplastics in environmental samples.
Identification and quantification of microplastics using Nile Red staining
Researchers tested Nile Red staining as a method for identifying and quantifying microplastics in environmental samples, finding it useful for rapid screening but noting limitations in distinguishing plastics from non-plastic particles.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
On the use of machine learning for microplastic identification from holographic phase-contrast signatures
This study applied machine learning to identify microplastic types from holographic phase-contrast imaging signatures, achieving rapid automated classification. Automated identification tools are important for scaling up microplastic monitoring in marine waters where manual identification is too slow and labor-intensive.
Nile Red Staining as a Subsidiary Method for Microplastic Quantifica-tion: A Comparison of Three Solvents and Factors Influencing Application Reliability
This study evaluated Nile Red fluorescent staining as a method for quantifying microplastics in environmental samples, comparing it to traditional identification techniques. The approach can help distinguish microplastics from organic particles more quickly and cost-effectively, supporting higher-throughput microplastic analysis in environmental monitoring programs.
Nile Red staining for the detection of microplastics: a comprehensive study on the emission spectra
This study systematically characterized how Nile Red fluorescence spectra vary across different polymer types, pigments, weathering states, and surface roughness, providing a more comprehensive reference for using Nile Red staining to identify microplastics in environmental samples.
Rapid detection and quantification of Nile Red-stained microplastic particles in sediment samples
Researchers developed a Nile Red staining method combined with automated fluorescence microscopy to rapidly detect and quantify microplastics in deep-sea sediment samples. The method significantly reduced analysis time compared to manual identification while maintaining accuracy, enabling higher-throughput monitoring of microplastic contamination in marine sediments.
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.
Dyeing to Know: Optimizing Solvents for Nile Red Fluorescence in Microplastics Analysis
Researchers investigated how the choice of solvent affects Nile Red fluorescence staining for microplastic identification, optimizing solvent conditions to improve the reliability of fluorescence-based classification of microplastic polymer types in environmental samples.
Modification of fluorescence staining method for small-sized microplastic quantification: Focus on the interference exclusion and exposure time optimization
Researchers optimized a Nile Red/DAPI fluorescence co-staining method for quantifying small microplastics, identifying key interference factors and exposure time parameters that significantly improve accuracy of microplastic detection.
Comprehensive assessment of factors influencing Nile red staining: Eliciting solutions for efficient microplastics analysis
Researchers conducted a comprehensive assessment of Nile red staining for microplastic analysis and found that wavelength, temperature, hydrogen peroxide treatment, NaCl addition, and plastic polymer type all significantly influence staining efficiency, proposing solutions to improve detection accuracy.