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Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector

Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of poll...

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Autores principales: Daunys, Gintautas, Šukienė, Laura, Vaitkevičius, Lukas, Valiulis, Gediminas, Sofiev, Mikhail, Šaulienė, Ingrida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951810/
https://www.ncbi.nlm.nih.gov/pubmed/33705418
http://dx.doi.org/10.1371/journal.pone.0247284
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author Daunys, Gintautas
Šukienė, Laura
Vaitkevičius, Lukas
Valiulis, Gediminas
Sofiev, Mikhail
Šaulienė, Ingrida
author_facet Daunys, Gintautas
Šukienė, Laura
Vaitkevičius, Lukas
Valiulis, Gediminas
Sofiev, Mikhail
Šaulienė, Ingrida
author_sort Daunys, Gintautas
collection PubMed
description Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors.
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spelling pubmed-79518102021-03-22 Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector Daunys, Gintautas Šukienė, Laura Vaitkevičius, Lukas Valiulis, Gediminas Sofiev, Mikhail Šaulienė, Ingrida PLoS One Research Article Automatically operating particle detection devices generate valuable data, but their use in routine aerobiology needs to be harmonized. The growing network of researchers using automatic pollen detectors has the challenge to develop new data processing systems, best suited for identification of pollen or spore from bioaerosol data obtained near-real-time. It is challenging to recognise all the particles in the atmospheric bioaerosol due to their diversity. In this study, we aimed to find the natural groupings of pollen data by using cluster analysis, with the intent to use these groupings for further interpretation of real-time bioaerosol measurements. The scattering and fluorescence data belonging to 29 types of pollen and spores were first acquired in the laboratory using Rapid-E automatic particle detector. Neural networks were used for primary data processing, and the resulting feature vectors were clustered for scattering and fluorescence modality. Scattering clusters results showed that pollen of the same plant taxa associates with the different clusters corresponding to particle shape and size properties. According to fluorescence clusters, pollen grouping highlighted the possibility to differentiate Dactylis and Secale genera in the Poaceae family. Fluorescent clusters played a more important role than scattering for separating unidentified fluorescent particles from tested pollen. The proposed clustering method aids in reducing the number of false-positive errors. Public Library of Science 2021-03-11 /pmc/articles/PMC7951810/ /pubmed/33705418 http://dx.doi.org/10.1371/journal.pone.0247284 Text en © 2021 Daunys et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Daunys, Gintautas
Šukienė, Laura
Vaitkevičius, Lukas
Valiulis, Gediminas
Sofiev, Mikhail
Šaulienė, Ingrida
Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
title Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
title_full Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
title_fullStr Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
title_full_unstemmed Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
title_short Clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
title_sort clustering approach for the analysis of the fluorescent bioaerosol collected by an automatic detector
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7951810/
https://www.ncbi.nlm.nih.gov/pubmed/33705418
http://dx.doi.org/10.1371/journal.pone.0247284
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