Machine learning driven methodology for enhanced nylon microplastic detection and characterization.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 Feb 2024
12 Feb 2024
Historique:
received:
01
12
2023
accepted:
07
02
2024
medline:
12
2
2024
pubmed:
12
2
2024
entrez:
11
2
2024
Statut:
epublish
Résumé
In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detection methodology has led to incomparability across studies. Addressing this gap, our current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectroscopy. This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine (SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm
Identifiants
pubmed: 38342944
doi: 10.1038/s41598-024-54003-1
pii: 10.1038/s41598-024-54003-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3464Subventions
Organisme : Science Foundation Ireland
ID : 21/PATH-S/9290
Pays : Ireland
Informations de copyright
© 2024. The Author(s).
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