We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
TUM-ParticleTyper: A detection and quantification tool for automated analysis of (Microplastic) particles and fibers
AI summary Read the abstract
TUM-ParticleTyper is a new computer program that automatically detects and counts microplastic particles in microscope images, reducing the time and human effort needed for analysis. The tool can also flag particles for chemical identification, helping standardize microplastic research.
TUM-ParticleTyper is a novel program for the automated detection, quantification and morphological characterization of fragments, including particles and fibers, in images from optical, fluorescence and electron microscopy (SEM). It can be used to automatically select targets for subsequent chemical analysis, e.g., Raman microscopy, or any other single particle identification method. The program was specifically developed and validated for the analysis of microplastic particles on gold coated polycarbonate filters. Our method development was supported by the design of a filter holder that minimizes filter roughness and facilitates enhanced focusing for better images and Raman measurements. The TUM-ParticleTyper software is tunable to the user's specific sample demands and can extract the morphological characteristics of detected objects (coordinates, Feret's diameter min / max, area and shape). Results are saved in csv-format and contours of detected objects are displayed as an overlay on the original image. Additionally, the program can stitch a set of images to create a full image out of several smaller ones. An additional useful feature is the inclusion of a statistical process to calculate the minimum number of particles that must be chemically identified to be representative of all particles localized on the substrate. The program performance was evaluated on genuine microplastic samples. The TUM-ParticleTyper software localizes particles using an adaptive threshold with results comparable to the "gold standard" method (manual localization by an expert) and surpasses the commonly used Otsu thresholding by doubling the rate of true positive localizations. This enables the analysis of a statistically significant number of particles on the filter selected by random sampling, measured via single point approach. This extreme reduction in measurement points was validated by comparison to chemical imaging, applying both procedures to the same area at comparable processing times. The single point approach was both faster and more accurate proving the applicability of the presented program.
More Papers Like This
Microplastic Spot Analyzer: Automated Image-Based Detection and Quantification of Microplastic Particles
AI summary Read the abstract
Scientists have created a new software tool that automatically spots and measures microplastic particles in microscope images, replacing the slow, error-prone process of counting them by hand. This matters because faster, more consistent microplastic detection could help researchers better track how much of this pollution is in our water, food, and environment, an important step toward understanding potential health risks. Note that this paper introduces the tool itself rather than new findings about microplastics' health effects.
Microplastic Spot Analyzer: Automated Image-Based Detection and Quantification of Microplastic Particles
AI summary Read the abstract
Scientists have created a new software tool that automatically spots and measures microplastic particles in microscope images, replacing the slow, error-prone process of counting them by hand. This matters because faster, more consistent microplastic detection makes it easier for researchers to study how much of this material is in our water, food, and environment, a key step toward understanding its potential health risks. Note that this paper introduces the tool itself rather than new findings about microplastics' health effects.
Microplastics_SEM
AI summary Read the abstract
Scientists developed a computer program that can automatically find and identify tiny plastic particles (called microplastics) in detailed microscope images. This new tool makes it much faster and more accurate to detect microplastics in things like food, water, and air samples. This matters because microplastics are everywhere in our environment and may affect human health, but until now it was very slow and difficult for researchers to study them properly.
A Handy Open-Source Application Based on Computer Vision and Machine Learning Algorithms to Count and Classify Microplastics
AI summary Read the abstract
An open-source computer vision application was developed to automatically count and classify microplastics in microscopy images, achieving accuracy comparable to manual counting while processing samples orders of magnitude faster, offering the scientific community a free tool to reduce the bottleneck of tedious visual microplastic enumeration.
Automatic Counting and Classification of Microplastic Particles
AI summary Read the abstract
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.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.