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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.
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.
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 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.
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.
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.
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 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.
Identification of marine microplastics by a combined method of principal component analysis and random forest for fluorescence spectrum processing
Researchers developed a combined principal component analysis and random forest method to identify microplastics from overlapping fluorescence spectra. The technique achieved 99.7% accuracy for component identification and a correlation coefficient exceeding 0.99 for predicting microplastic concentrations. The model, initially trained on commercial plastic samples, was also successfully applied to identify real marine microplastics.
[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.
Machine-Learning-Accelerated Prediction of Water Quality Criteria for Microplastics
Researchers developed a machine learning framework to predict microplastic toxicity in aquatic organisms and derive water quality criteria for five common polymer types. The random forest model outperformed other algorithms, with particle size, density, and aquatic species group accounting for 72% of prediction variability. The study found that polystyrene and PET exhibited the greatest toxicity, and that microplastics were generally more toxic in freshwater than saltwater environments.
Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.
Researchers developed a near-infrared spectroscopy method combined with machine learning classifiers -- including PLS-DA, random forest, and XGBoost -- to rapidly identify both colored and colorless plastic fragments across different polymer types, thicknesses, and environmental backgrounds. The approach improved detection of colorless plastics that are typically underestimated in environmental surveys, with random forest achieving the highest classification accuracy.
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.
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.
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.
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.
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.
Tall Trees and Small Plastics. Using Random Forest Classification to Identify Microplastic Pollution in Surface Soil Samples
Researchers used machine learning (random forest classification) to identify and distinguish twenty types of plastic particles in soil samples from agricultural land. Developing accurate, automated detection methods for microplastics in soil is essential for large-scale environmental monitoring.
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.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.