We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
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
Two machine learning approaches using Nile red fluorescence staining and automated image analysis were tested for robustness on marine microplastics of multiple polymer types and weathering states, finding performance varied with particle heterogeneity and environmental aging.
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.
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.
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.
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.
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.
[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.
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.
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.
A new approach for routine quantification of microplastics using Nile Red and automated software (MP-VAT)
Researchers developed a new workflow combining Nile Red fluorescence staining with automated image analysis software (MP-VAT) to rapidly quantify microplastics in environmental samples, reducing the labor and subjectivity of manual counting methods. The automated approach improves throughput and reproducibility for routine microplastic monitoring applications.
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.
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.
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.
A rapid-screening approach to detect and quantify microplastics based on fluorescent tagging with Nile Red
Researchers developed a rapid fluorescent screening method using Nile Red dye to detect and quantify microplastics in environmental samples, finding it significantly faster than conventional methods while maintaining reasonable accuracy.
A methodology for the fast identification and monitoring of microplastics in environmental samples using random decision forest classifiers
Researchers developed a methodology using random decision forest classifiers for the fast identification and monitoring of microplastics in environmental samples. The approach provides a machine learning-based tool to accelerate microplastic detection and reduce the analytical burden of characterising particles across diverse environmental matrices.
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.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.