Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains
Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have po...
Published in: | Analytical and Bioanalytical Chemistry |
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Language: | English |
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Humboldt-Universität zu Berlin
2020
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Online Access: | http://edoc.hu-berlin.de/18452/24372 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/24372-1 https://doi.org/10.18452/23711 https://doi.org/10.1007/s00216-020-02628-2 |
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fthuberlin:oai:edoc.hu-berlin.de:18452/24372 2023-12-03T10:29:20+01:00 Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Bağcıoğlu, Murat Kohler, Achim Ohlson, Mikael Fjellheim, Siri Kneipp, Janina 2020-04-29 application/pdf http://edoc.hu-berlin.de/18452/24372 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/24372-1 https://doi.org/10.18452/23711 https://doi.org/10.1007/s00216-020-02628-2 eng eng Humboldt-Universität zu Berlin http://edoc.hu-berlin.de/18452/24372 urn:nbn:de:kobv:11-110-18452/24372-1 http://dx.doi.org/10.18452/23711 1618-2650 doi:10.1007/s00216-020-02628-2 (CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ Poaceae Pollen Fourier-transform infrared (FTIR) microspectroscopy Mie scattering Paraffin Non-negative matrix factorization Extended multiplicative signal correction Partial least squares-discriminant analysis Machine learning 540 Chemie und zugeordnete Wissenschaften ddc:540 article doc-type:article publishedVersion 2020 fthuberlin https://doi.org/10.18452/2371110.1007/s00216-020-02628-2 2023-11-05T23:35:54Z Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization. Peer Reviewed Article in Journal/Newspaper Poa alpina Open-Access-Publikationsserver der Humboldt-Universität: edoc-Server Analytical and Bioanalytical Chemistry 412 24 6459 6474 |
institution |
Open Polar |
collection |
Open-Access-Publikationsserver der Humboldt-Universität: edoc-Server |
op_collection_id |
fthuberlin |
language |
English |
topic |
Poaceae Pollen Fourier-transform infrared (FTIR) microspectroscopy Mie scattering Paraffin Non-negative matrix factorization Extended multiplicative signal correction Partial least squares-discriminant analysis Machine learning 540 Chemie und zugeordnete Wissenschaften ddc:540 |
spellingShingle |
Poaceae Pollen Fourier-transform infrared (FTIR) microspectroscopy Mie scattering Paraffin Non-negative matrix factorization Extended multiplicative signal correction Partial least squares-discriminant analysis Machine learning 540 Chemie und zugeordnete Wissenschaften ddc:540 Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Bağcıoğlu, Murat Kohler, Achim Ohlson, Mikael Fjellheim, Siri Kneipp, Janina Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains |
topic_facet |
Poaceae Pollen Fourier-transform infrared (FTIR) microspectroscopy Mie scattering Paraffin Non-negative matrix factorization Extended multiplicative signal correction Partial least squares-discriminant analysis Machine learning 540 Chemie und zugeordnete Wissenschaften ddc:540 |
description |
Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization. Peer Reviewed |
format |
Article in Journal/Newspaper |
author |
Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Bağcıoğlu, Murat Kohler, Achim Ohlson, Mikael Fjellheim, Siri Kneipp, Janina |
author_facet |
Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Bağcıoğlu, Murat Kohler, Achim Ohlson, Mikael Fjellheim, Siri Kneipp, Janina |
author_sort |
Diehn, Sabrina |
title |
Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains |
title_short |
Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains |
title_full |
Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains |
title_fullStr |
Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains |
title_full_unstemmed |
Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains |
title_sort |
discrimination of grass pollen of different species by ftir spectroscopy of individual pollen grains |
publisher |
Humboldt-Universität zu Berlin |
publishDate |
2020 |
url |
http://edoc.hu-berlin.de/18452/24372 https://nbn-resolving.org/urn:nbn:de:kobv:11-110-18452/24372-1 https://doi.org/10.18452/23711 https://doi.org/10.1007/s00216-020-02628-2 |
genre |
Poa alpina |
genre_facet |
Poa alpina |
op_relation |
http://edoc.hu-berlin.de/18452/24372 urn:nbn:de:kobv:11-110-18452/24372-1 http://dx.doi.org/10.18452/23711 1618-2650 doi:10.1007/s00216-020-02628-2 |
op_rights |
(CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.18452/2371110.1007/s00216-020-02628-2 |
container_title |
Analytical and Bioanalytical Chemistry |
container_volume |
412 |
container_issue |
24 |
container_start_page |
6459 |
op_container_end_page |
6474 |
_version_ |
1784254609647206400 |