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Microplastic detection and identification by Nile red staining: towards a semi-automated, cost- and time-effective technique
Meyers, N.; Catarino, A.I.; Declercq, A.M.; Brenan, A.; Devriese, L.; Vandegehuchte, M.; De Witte, B.; Janssen, C.; Everaert, G. (2022). Microplastic detection and identification by Nile red staining: towards a semi-automated, cost- and time-effective technique. Sci. Total Environ. 823: 153441. https://dx.doi.org/10.1016/j.scitotenv.2022.153441
In: Science of the Total Environment. Elsevier: Amsterdam. ISSN 0048-9697; e-ISSN 1879-1026, meer
Peer reviewed article  
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Trefwoord
    Marien/Kust
Author keywords
    Microplastic classification; Automation; Machine learning; Nile red fluorescence; Image processing; RGB
Auteurs  Top 
  • Meyers, N., meer
  • Catarino, A.I., meer
  • Declercq, A.M., meer
  • Brenan, A., meer
  • Devriese, L., meer
  • Vandegehuchte, M., meer
Abstract
    Microplastic pollution is an issue of concern due to the accumulation rates in the marine environment combined with the limited knowledge about their abundance, distribution and associated environmental impacts. However, surveying and monitoring microplastics in the environment can be time consuming and costly. The development of cost- and time-effective methods is imperative to overcome some of the current critical bottlenecks in microplastic detection and identification, and to advance microplastics research. Here, an innovative approach for microplastic analysis is presented that combines the advantages of high-throughput screening with those of automation. The proposed approach used Red Green Blue (RGB) data extracted from photos of Nile red-fluorescently stained microplastics (50–1200 μm) to train and validate a ‘Plastic Detection Model’ (PDM) and a ‘Polymer Identification Model’ (PIM). These two supervised machine learning models predicted with high accuracy the plastic or natural origin of particles (95.8%), and the polymer types of the microplastics (88.1%). The applicability of the PDM and the PIM was demonstrated by successfully using the models to detect (92.7%) and identify (80%) plastic particles in spiked environmental samples that underwent laboratorial processing. The classification models represent a semi-automated, high-throughput and reproducible method to characterize microplastics in a straightforward, cost- and time-effective yet reliable way.
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