IMIS

Publicaties | Instituten | Personen | Datasets | Projecten | Kaarten
[ meld een fout in dit record ] Print deze pagina

RGB-statistics derived from Nile red-stained reference plastics for the construction of the PIM (Polymer Identification Model)
Citeerbaar als data publicatie
Meyers, N.; Catarino, A.I.; Declercq, A.M.; Brenan, A.; Devriese, L.; Vandegehuchte, M.; 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; (2021): RGB-statistics derived from Nile red-stained reference plastics for the construction of the PIM (Polymer Identification Model). Marine Data Archive. https://doi.org/10.14284/511
Contact: Meyers, Nelle

Beschikbaarheid: Creative Commons License Deze dataset valt onder een Creative Commons Naamsvermelding 4.0 Internationaal-licentie.

Beschrijving
Dataset containing RGB-statistics extracted from photographed fluorescent reference plastics stained with Nile red. The most abundantly produced plastic polymers worldwide considered for this dataset. The spectral data was used to construct a supervised machine learning model that allows to accurately identify the polymer types microplastics belong to, in a cost- and time-efficient way. meer

The dataset was built to train and validate the ‘Polymer Identification Model’ (PIM) in R and contains Red, Green and Blue (RGB) statistics extracted from Nile red-stained reference plastics (50-1200 μm) photographed under three different microscope filters (UV: Filter System A S, BP 340-380 nm; blue: Filter System I3 S, BP 450-490 nm; and green: Filter system N2.1 S, BP 515-560 nm) (LEICA DM 1000). Image analysis to extract all RGB-values was performed using a macro in ImageJ. The supervised machine learning model (CART algorithm) trained by and validated with this dataset predicts with high accuracy the polymer types microplastics belong to, in a cost- and time-efficient way. RGB statistics of the most abundantly produced plastic polymers worldwide were compiled into the dataset. The statistics itself were calculated per reference particle as the 10th, 50th and 90th percentile as well as the mean of each of the three different color components extracted from all pixels laying along the maximum Feret diameter of that photographed particle. The dataset contains RGB-statistics calculated through image analysis of 210 plastic particles belonging to seven polymer types (Nylon, PE, PET, PP, PS, PUR and PVC) (30 particles/polymer type), where 168 particles (1/4) were randomly selected and used to serve as training data (24 particles/polymer) (worksheet tab ‘training data’), while the remaining 42 particles (1/5) were kept as independent validation data (6 particles/polymer type) (worksheet tab ‘training data’).

Scope
Thema's:
Milieu-kwaliteit / vervuiling
Kernwoorden:
Marien/Kust, Zoet water, Brak water, Fluorescence microscopy, Machine learning, Microplastics, Polymer identification method, RGB colour data, World

Geografische spreiding

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), meerdata creatordata creator
Vlaamse overheid; Beleidsdomein Landbouw en Visserij; Instituut voor landbouw-, visserij en voedingsonderzoek (ILVO), meerdata creator
Universiteit Gent; Faculteit Bio-ingenieurswetenschappen; Vakgroep Dierwetenschappen en Aquatische Ecologie; Laboratorium voor Milieutoxicologie (GhEnToxLab), meerdata creator
Universiteit Gent; Faculteit Bio-ingenieurswetenschappen; Vakgroep Dierwetenschappen en Aquatische Ecologie; Laboratorium voor Aquacultuur en Artemia Reference Center (ARC), meerdata creator

Gerelateerde datasets
Andere relatie:
RGB-statistics derived from Nile red-stained reference plastics for the construction of the PDM (Plastics Detection Model), meer

Project
PhD Developing and optimising cost- and time-effective methods for the detection and identification of microplastics in the marine environment, meer
ANDROMEDA: Analysis techniques for quantifying nano-and microplastic particles and their degradation in the marine environment, meer

Dataset status: Afgelopen
Data type: Data
Data oorsprong: Onderzoek: labo-experiment
Metadatarecord aangemaakt: 2021-08-23
Informatie laatst gewijzigd: 2021-09-28
Alle informatie in het Integrated Marine Information System (IMIS) valt onder het VLIZ Privacy beleid