Combining Chemical Information From Grass Pollen in Multimodal Characterization
The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical d...
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Online Access: | https://hdl.handle.net/11250/2688716 https://doi.org/10.3389/fpls.2019.01788 |
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ftnofima:oai:nofima.brage.unit.no:11250/2688716 2023-05-15T18:01:40+02:00 Combining Chemical Information From Grass Pollen in Multimodal Characterization Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Seifert, Stephan Bağcıoğlu, Murat Ohlson, Mikael Weidner, Steffen Fjellheim, Siri Kohler, Achim Kneipp, Janina 2020 application/pdf https://hdl.handle.net/11250/2688716 https://doi.org/10.3389/fpls.2019.01788 eng eng EC/FP7/328289 ERC-European Research Council: 259432 Frontiers in Plant Science. 2020, 10 . urn:issn:1664-462X https://hdl.handle.net/11250/2688716 https://doi.org/10.3389/fpls.2019.01788 cristin:1820588 18 10 Frontiers in Plant Science Fouriertransform infrared spectroscopy Consensus principal component analysis Pollen Peer reviewed Journal article 2020 ftnofima https://doi.org/10.3389/fpls.2019.01788 2022-11-18T06:50:58Z The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters. publishedVersion Article in Journal/Newspaper Poa alpina Nofima Knowledge Archive (Brage) Frontiers in Plant Science 10 |
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Open Polar |
collection |
Nofima Knowledge Archive (Brage) |
op_collection_id |
ftnofima |
language |
English |
topic |
Fouriertransform infrared spectroscopy Consensus principal component analysis Pollen |
spellingShingle |
Fouriertransform infrared spectroscopy Consensus principal component analysis Pollen Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Seifert, Stephan Bağcıoğlu, Murat Ohlson, Mikael Weidner, Steffen Fjellheim, Siri Kohler, Achim Kneipp, Janina Combining Chemical Information From Grass Pollen in Multimodal Characterization |
topic_facet |
Fouriertransform infrared spectroscopy Consensus principal component analysis Pollen |
description |
The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Seifert, Stephan Bağcıoğlu, Murat Ohlson, Mikael Weidner, Steffen Fjellheim, Siri Kohler, Achim Kneipp, Janina |
author_facet |
Diehn, Sabrina Zimmermann, Boris Tafintseva, Valeria Seifert, Stephan Bağcıoğlu, Murat Ohlson, Mikael Weidner, Steffen Fjellheim, Siri Kohler, Achim Kneipp, Janina |
author_sort |
Diehn, Sabrina |
title |
Combining Chemical Information From Grass Pollen in Multimodal Characterization |
title_short |
Combining Chemical Information From Grass Pollen in Multimodal Characterization |
title_full |
Combining Chemical Information From Grass Pollen in Multimodal Characterization |
title_fullStr |
Combining Chemical Information From Grass Pollen in Multimodal Characterization |
title_full_unstemmed |
Combining Chemical Information From Grass Pollen in Multimodal Characterization |
title_sort |
combining chemical information from grass pollen in multimodal characterization |
publishDate |
2020 |
url |
https://hdl.handle.net/11250/2688716 https://doi.org/10.3389/fpls.2019.01788 |
genre |
Poa alpina |
genre_facet |
Poa alpina |
op_source |
18 10 Frontiers in Plant Science |
op_relation |
EC/FP7/328289 ERC-European Research Council: 259432 Frontiers in Plant Science. 2020, 10 . urn:issn:1664-462X https://hdl.handle.net/11250/2688716 https://doi.org/10.3389/fpls.2019.01788 cristin:1820588 |
op_doi |
https://doi.org/10.3389/fpls.2019.01788 |
container_title |
Frontiers in Plant Science |
container_volume |
10 |
_version_ |
1766171173060608000 |