Comparison of two rapid automated analysis tools for large FTIR microplastic datasets.
Automated microplastic analysis
Bayreuth Particle Finder (BPF)
FTIR
Freshwater samples
Seawater samples
siMPle
Journal
Analytical and bioanalytical chemistry
ISSN: 1618-2650
Titre abrégé: Anal Bioanal Chem
Pays: Germany
ID NLM: 101134327
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
02
11
2022
accepted:
23
02
2023
revised:
16
02
2023
medline:
21
3
2023
pubmed:
21
3
2023
entrez:
20
3
2023
Statut:
ppublish
Résumé
One of the biggest issues in microplastic (MP, plastic items <5 mm) research is the lack of standardisation and harmonisation in all fields, reaching from sampling methodology to sample purification, analytical methods and data analysis. This hampers comparability as well as reproducibility among studies. Concerning chemical analysis of MPs, Fourier-transform infrared (FTIR) spectroscocopy is one of the most powerful tools. Here, focal plane array (FPA) based micro-FTIR (µFTIR) imaging allows for rapid measurement and identification without manual preselection of putative MP and therefore enables large sample throughputs with high spatial resolution. The resulting huge datasets necessitate automated algorithms for data analysis in a reasonable time frame. Although solutions are available, little is known about the comparability or the level of reliability of their output. For the first time, within our study, we compare two well-established and frequently applied data analysis algorithms in regard to results in abundance, polymer composition and size distributions of MP (11-500 µm) derived from selected environmental water samples: (a) the siMPle analysis tool (systematic identification of MicroPlastics in the environment) in combination with MPAPP (MicroPlastic Automated Particle/fibre analysis Pipeline) and (b) the BPF (Bayreuth Particle Finder). The results of our comparison show an overall good accordance but also indicate discrepancies concerning certain polymer types/clusters as well as the smallest MP size classes. Our study further demonstrates that a detailed comparison of MP algorithms is an essential prerequisite for a better comparability of MP data.
Identifiants
pubmed: 36939884
doi: 10.1007/s00216-023-04630-w
pii: 10.1007/s00216-023-04630-w
pmc: PMC10284987
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2975-2987Subventions
Organisme : Bundesministerium für Bildung und Forschung
ID : 03F0789A
Organisme : Bundesministerium für Bildung und Forschung
ID : 03F0789B
Informations de copyright
© 2023. The Author(s).
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