PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Klimczak, L.J.* ; Ebner von Eschenbach, C. ; Thompson, P.M.* ; Buters, J.T.M. ; Mueller, G.A.*

Mixture analyses of air-sampled pollen extracts can accurately differentiate pollen taxa.

Atmos. Environ. 243:117746 (2020)
Postprint DOI
Open Access Green
The daily pollen forecast provides crucial information for allergic patients to avoid exposure to specific pollen. Pollen counts are typically measured with air samplers and analyzed with microscopy by trained experts. In contrast, this study evaluated the effectiveness of identifying the component pollens using the metabolites extracted from an air-sampled pollen mixture. Ambient air-sampled pollen from Munich in 2016 and 2017 was visually identified from reference pollens and extracts were prepared. The extracts were lyophilized, rehydrated in optimal NMR buffers, and filtered to remove large proteins. NMR spectra were analyzed for pollen associated metabolites. Regression and decision-tree based algorithms using the concentration of metabolites calculated from the NMR spectra outperformed algorithms using the NMR spectra themselves as input data for pollen identification. Categorical prediction algorithms trained for low, medium, high, and very high pollen count groups had accuracies of 74% for the tree, 82% for the grass, and 93% for the weed pollen count. Deep learning models using convolutional neural networks performed better than regression models using NMR spectral input, and were the overall best method in terms of relative error and classification accuracy (86% for tree, 89% for grass, and 93% for weed pollen count). This study demonstrates that NMR spectra of air-sampled pollen extracts can be used in an automated fashion to provide taxa and type-specific measures of the daily pollen count.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
4.039
1.494
1
5
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Pollen ; Nmr ; Metabolomics ; Mixtures ; Exposure ; Aerobiology; Airborne Pollen; Identification; Location
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 1352-2310
e-ISSN 1873-2844
Zeitschrift Atmospheric Environment
Quellenangaben Band: 243, Heft: , Seiten: , Artikelnummer: 117746 Supplement: ,
Verlag Elsevier
Verlagsort The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Allergy Research (IAF)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Allergy
PSP-Element(e) G-505400-001
Förderungen State of North Carolina
Intramural Research Program of the National Institute of Environmental Health Sciences
DOI 10.1016/j.atmosenv.2020.117746
WOS ID WOS:000587335700005
Scopus ID 85089998561
Erfassungsdatum 2020-09-15
[➜Korrektur melden]