Publicaties | Instituten | Personen | Datasets | Projecten | Kaarten | ||||
RGB datasets for machine learning-based microplastic analysis - update
Citeerbaar als data publicatie
Meyers, N.; De Witte, B.; Janssen, C.; Everaert, G.; Flanders Marine Institute (VLIZ); Flanders Research Institute for Agriculture, Fisheries and Food (ILVO); Ghent University Laboratory for Environmental Toxicology (GhEnToxLab): Belgium; (2024): RGB datasets for machine learning-based microplastic analysis - update. Marine Data Archive. https://doi.org/10.14284/665
Contact:
Meyers, Nelle Beschikbaarheid:
![]() Beschrijving
This dataset features RGB statistics extracted from Nile red-stained particles, serving to train a 'Plastic Detection Model' and a 'Polymer Identification Model' using supervised machine learning techniques. By accurately discerning between plastic and natural particles, and distinguishing between different plastic polymers, the models facilitate efficient microplastic detection and identification. meer
Datasets containing RGB statistics extracted from photographed fluorescent reference particles stained with Nile red. The most abundantly produced plastic polymers worldwide as well as natural materials with high prevalence in the marine environment were considered for these datasets. The spectral data was used to construct two supervised machine learning models, i.e. a ‘Plastic Detection Model’ (PDM) and a ‘Polymer Identification Model’ (PIM), based on random forest algorithms. The PDM allows to accurately distinguish plastic from natural particles, while the PIM allows to distinguish different plastic polymers, in a cost- and time-efficient way. The datasets contain Red, Green and Blue (RGB) statistics extracted from Nile red-stained reference particles (50-1200 μm) photographed under three different microscope filters (blue, green and UV). Four different datasets can be found, two for each model (PDM vs. PIM), based on photographs acquired with two different types of microscope (Leica DM 1000 fluorescence microscope vs. Leica M205 FA fluorescence stereomicroscope). The datasets represent an updated version of earlier published RGB datasets, now containing 135 – 200 particles per polymer category (PIM), and 420-500 per particle type (plastic/organic). Scope Thema's: Milieu-kwaliteit / vervuiling Kernwoorden: Marien/Kust, Automated detection, Fluorescent colouration, Machine learning, Microplastics, Nile red staining, Random forest models, RGB colour data, World Geografische spreiding World [Marine Regions] Parameter
RGB (Red, Green, Blue) colour component means and percentiles Methode
RGB (Red, Green, Blue) colour component means and percentiles: Fluorescence microscopy combined with image analysis. Bijdrage door
Vlaams Instituut voor de Zee (VLIZ), meer, data creator
Universiteit Gent; Faculteit Bio-ingenieurswetenschappen; Vakgroep Dierwetenschappen en Aquatische Ecologie; Laboratorium voor Milieutoxicologie (GhEnToxLab), meer, data creator, data creator
Vlaamse overheid; Beleidsdomein Landbouw en Visserij; Instituut voor landbouw-, visserij en voedingsonderzoek (ILVO), meer, data creator
Gerelateerde datasets
Project
ANDROMEDA: Analysis techniques for quantifying nano-and microplastic particles and their degradation in the marine environment, meer
Publicatie
Gebaseerd op deze dataset
De Witte, B. et al. (2024). ANDROMEDA portfolio of microplastics analyses protocols. ANDROMEDA Deliverable 5.5. JPI Oceans ANDROMEDA project: [s.l.]. 88 pp., meer
Dataset status: Afgelopen
Data type: Data
Data oorsprong: Onderzoek: labo-experiment
Metadatarecord aangemaakt: 2024-04-05
Informatie laatst gewijzigd: 2024-04-05
|