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2020-01-31Zeitschriftenartikel DOI: 10.18452/21337
Combining Chemical Information From Grass Pollen in Multimodal Characterization
dc.contributor.authorDiehn, Sabrina
dc.contributor.authorZimmermann, Boris
dc.contributor.authorTafintseva, Valeria
dc.contributor.authorSeifert, Stephan
dc.contributor.authorBağcioğlu, Murat
dc.contributor.authorOhlson, Mikael
dc.contributor.authorWeidner, Steffen
dc.contributor.authorFjellheim, Siri
dc.contributor.authorKohler, Achim
dc.contributor.authorKneipp, Janina
dc.date.accessioned2020-04-07T08:12:29Z
dc.date.available2020-04-07T08:12:29Z
dc.date.issued2020-01-31none
dc.identifier.other10.3389/fpls.2019.01788
dc.identifier.urihttp://edoc.hu-berlin.de/18452/22078
dc.description.abstractThe 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.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectpolleneng
dc.subjectconsensus principal component analysiseng
dc.subjectANOVA simultaneous component analysiseng
dc.subjectFouriertransform infrared spectroscopyeng
dc.subjectmatrix assisted laser desorption/ionization mass spectrometryeng
dc.subjectsurfaceenhanced Raman scatteringeng
dc.subjectRaman spectroscopyeng
dc.subjectPoa alpinaeng
dc.subject.ddc570 Biologienone
dc.titleCombining Chemical Information From Grass Pollen in Multimodal Characterizationnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/22078-8
dc.identifier.doihttp://dx.doi.org/10.18452/21337
dc.type.versionpublishedVersionnone
local.edoc.container-titleFrontiers in plant sciencenone
local.edoc.pages18none
local.edoc.anmerkungThis article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameFrontiers Medianone
local.edoc.container-publisher-placeLausannenone
local.edoc.container-volume10none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber1788
dc.identifier.eissn1664-462X

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