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Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples

  • Hyperspectral imaging of environmental samples with infrared microscopes is one of the preferred methods to find and characterize microplastics. Particles can be quantified in terms of number, size and size distribution. Their shape can be studied and the substances can be identified. Interpretation of the collected spectra is a typical problem encountered during the analysis. The image datasets are large and contain spectra of countless particles of natural and synthetic origin. To supplement existing Analysis pipelines, exploratory multivariate data analysis was tested on two independent datasets. Dimensionality reduction with principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) was used as a core concept. It allowed for improved visual accessibility of the data and created a chemical two-dimensional image of the sample. Spectra belonging to particles could be separated from blank spectra, reducing the amount of data significantly. SelectedHyperspectral imaging of environmental samples with infrared microscopes is one of the preferred methods to find and characterize microplastics. Particles can be quantified in terms of number, size and size distribution. Their shape can be studied and the substances can be identified. Interpretation of the collected spectra is a typical problem encountered during the analysis. The image datasets are large and contain spectra of countless particles of natural and synthetic origin. To supplement existing Analysis pipelines, exploratory multivariate data analysis was tested on two independent datasets. Dimensionality reduction with principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) was used as a core concept. It allowed for improved visual accessibility of the data and created a chemical two-dimensional image of the sample. Spectra belonging to particles could be separated from blank spectra, reducing the amount of data significantly. Selected spectra were further studied, also applying PCA and UMAP. Groups of similar spectra were identified by cluster analysis using k-means, density based, and interactive manual clustering. Most clusters could be assigned to chemical species based on reference spectra. While the results support findings obtained with a ‘targeted analysis’ based on automated library search, exploratory analysis points the attention towards the group of unidientified spectra that remained and are otherwise easily overlooked.zeige mehrzeige weniger

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Metadaten
Autor*innen:Lukas WanderORCiD, A. Vianello, J. Vollertsen, F. Westad, Ulrike BraunORCiD, Andrea PaulORCiD
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Analytical Methods
Jahr der Erstveröffentlichung:2020
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
6 Materialchemie
6 Materialchemie / 6.6 Physik und chemische Analytik der Polymere
Verlag:Royal Society of Chemistry
Jahrgang/Band:12
Ausgabe/Heft:6
Erste Seite:781
Letzte Seite:791
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Sanitär- und Kommunaltechnik; Umwelttechnik
Freie Schlagwörter:Exploratory analysis; FTIR; Microplastics
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Umwelt
DOI:10.1039/c9ay02483b
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:17.02.2020
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:05.03.2020
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