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Papers
61,005 resultsShowing papers similar to Towards reliable data: Validation of a machine learning-based approach for microplastics analysis in marine organisms using Nile red staining
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
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.
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.
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.
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.
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.
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.
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.
Analyzing microplastics with Nile Red: Emerging trends, challenges, and prospects
This review evaluates the Nile Red staining technique as an analytical method for identifying and quantifying microplastics in environmental samples. The study concludes that while Nile Red has emerged as a viable low-cost alternative to visual identification for microplastics research, not everything that fluoresces is plastic, so additional spectroscopic analysis is needed to validate results.
Exploring the Efficacy of Nile Red in Microplastic Quantification: A Costaining Approach
This study assessed the effectiveness of Nile Red, a fluorescent dye commonly used to detect microplastics, by comparing it with other staining approaches and evaluating detection accuracy. The research found that costaining strategies and careful protocol standardization can improve the reliability of Nile Red-based microplastic quantification.
Nile Red staining for detecting microplastics in biota: Preliminary evidence
Nile Red fluorescent staining was tested for identifying microplastics in biological tissue samples, finding that it successfully highlighted plastic particles in fish guts and bivalve tissues with minimal interference from digested organic residues, supporting its use as a quick screening tool before confirmatory spectroscopy.
Microplastic and nanoplastic analysis methods, tests and reference materials
Researchers described a workflow combining a streamlined experimental setup with automated image analysis to quantify marine microplastic debris, addressing the limitations of labor-intensive manual counting methods that currently prevent scalable and consistent global plastic monitoring.
[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.
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.
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.
Sampling, Isolating and Identifying Microplastics Ingested by Fish and Invertebrates *
This methodological review critically evaluated sampling, isolation, and identification techniques for microplastics ingested by fish and invertebrates, identifying common sources of error including contamination during processing, particle loss, and misidentification — and recommending standardized protocols.
Rapid detection of microplastic contamination using Nile red fluorescent tagging
Researchers developed a rapid microplastic detection method using Nile Red (NR) fluorescent staining combined with zinc chloride density-based extraction and filtration for analysis of coastal marine sediment samples. The approach was cross-validated against conventional light microscopy, demonstrating improved speed and sensitivity for identifying microplastics of various sizes in environmental sediment matrices.
New techniques for the detection of microplastics in sediments and field collected organisms
Researchers developed new techniques for detecting microplastics in sediment samples and for collecting particles in the field, improving the reliability and sensitivity of methods used to monitor environmental microplastic contamination.
Rapid methods for the quantification of ingested nano-and microplastics in marine fish by imaging flow cytometry
Researchers developed a rapid, high-throughput method using imaging flow cytometry to quantify nano- and microplastics ingested by marine fish. The optimized technique uses Nile Red fluorescent staining and morphology-based corrections to accurately count plastic particles, providing a faster and more reliable alternative to conventional detection methods for ecological risk assessments.
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.
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
This review examines how machine learning techniques including neural networks and random forests are being applied to microplastic detection, classification, and ecological risk assessment, demonstrating faster and more accurate results than traditional analytical methods. The authors identify data standardization and model interpretability as key challenges for broader adoption.
Recent advances in the analysis methodologies for microplastics in aquatic organisms: current knowledge and research challenges
This review examines recent advances in analytical methods for detecting and quantifying microplastics in aquatic organisms, identifying key sources of variability across studies and outlining research challenges needed to improve comparability and standardization.
Towards the suitable monitoring of ingestion of microplastics by marine biota: A review
This review assessed various monitoring methods for detecting microplastic ingestion by marine biota, comparing laboratory and field-based approaches. The authors recommend method selection based on organism type and research question and call for more consistent reporting standards to enable cross-study comparison.