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 Efficient Prediction of Microplastic Counts from Mass Measurements
ClearEfficient Prediction of Microplastic Counts from Mass Measurements
Researchers evaluated machine learning models including linear regression, kernel ridge regression, and decision trees for predicting microplastic particle counts from aggregate mass measurements, testing on synthetic and experimental datasets. They found that kernel ridge regression performed best, with lower prediction error for larger and more homogeneous samples, and that organic contamination did not substantially reduce predictive accuracy.
An Accurate Size-Probability Distribution Method for Converting Microplastic Counts to Mass
Researchers developed a size-probability distribution method to convert microplastic particle counts into mass estimates without requiring detailed morphological measurements for every particle, addressing a key gap in environmental monitoring where mass-based reporting is needed but count-based data is more commonly generated.
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 Mass Conversion for Environmental Microplastics of Diverse Shapes
Researchers developed a faster and more accurate method for converting microplastic counts into mass estimates, which is critical for calculating how much plastic rivers carry to the ocean. Using deep learning to classify microplastic shapes and a new approach to estimating thickness, the models reduced estimation errors by sevenfold compared to previous methods while saving about two hours per hundred particles analyzed.
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.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
A novel approach for the quantification of the mass of micro and nanoplastic particles from filter samples
Researchers developed a novel gravimetric approach to quantify the mass of micro- and nanoplastic particles collected on filter membranes, complementing existing count-based spectroscopic methods. The method enabled mass estimation from filter samples without requiring individual particle analysis, providing a faster approach for tracking microplastic mass loads in environmental monitoring.
Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics
Scientists developed a new microplastic detection system that combines tiny droplet-based testing with machine learning to quickly identify and classify microplastic particles. This portable system can accurately detect microplastics on-site without expensive lab equipment, which could make widespread environmental and food safety monitoring much more practical.
An introduction to machine learning tools for the analysis of microplastics in complex matrices
This paper introduces machine learning tools that can speed up the identification and counting of microplastics in complex samples like soil and water. While focused on analytical methods rather than health effects, faster and more accurate detection of microplastics is essential for understanding how much exposure humans actually face through food, water, and the environment.
SMACC: A System for Microplastics Automatic Counting and Classification
Researchers developed an automated computer vision system (SMACC) that uses image analysis to count and classify plastic particles in beach samples, demonstrating that machine learning can substantially reduce the time and effort required for large-scale beach microplastic monitoring.
Identification and velocity measurement of microplastics based on machine learning
Researchers developed a machine learning framework to simultaneously track multiple microplastics in water and measure their terminal settling velocities, capturing particle interaction dynamics that conventional single-particle tracking methods miss.
[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.
Prediction of Settling Velocity of Microplastics by Multiple Machine-Learning Methods
Researchers developed machine learning models to predict the settling velocity of microplastics in water, using particle shape, size, and density as inputs. The models outperformed traditional empirical equations, providing a more accurate tool for modeling microplastic transport and sedimentation.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Automatic Counting and Classification of Microplastic Particles
Researchers developed an automatic system for counting and classifying microplastic particles in marine samples, applying image analysis techniques to address the growing problem of plastic debris entering the food chain via marine species ingestion.
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning
Researchers used machine learning to predict microplastic levels in ocean surface waters and identify the key factors driving contamination. Their models found that geographic location, ocean currents, and proximity to populated coastlines were major predictors of microplastic abundance. This approach could help scientists map pollution hotspots without costly and time-consuming physical sampling.
Machine Learning to Predict the Adsorption Capacity of Microplastics
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
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.