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Klassifizierung von Pollenproben mit spektroskopischen und spektrometrischen Methoden und multivariater Statistik
| Content Provider | Semantic Scholar |
|---|---|
| Author | Seifert, Stephan |
| Copyright Year | 2016 |
| Abstract | The investigation of pollen grain samples for characterization, classification and identification is still an analytical task that is both challenging and time-consuming, since it is mainly based on the morphological characteristics of the pollen grains. In order to develop approaches that are based on extensive molecular information and that lead to an automated classification, in this thesis, a combination of spectroscopic and spectrometric tools is discussed. It is already known that a fingerprint analysis of the chemical composition of pollen samples can be accomplished by Raman and infrared spectroscopies. Here, surface-enhanced Raman scattering (SERS), matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS), and Fourier transform infrared (FTIR) spectroscopy of single pollen grains are successfully applied for the taxonomic classification of pollen samples. It is demonstrated that SERS provides several advantages compared to normal Raman scattering for pollen identification and reveals species-specific differences in the pollen phenome that display in the pollen cells. Specifically, classification and pre-selection of SERS spectra with artificial neural networks (ANN) are shown. MALDI-TOF MS of pollen samples yields fingerprints that can be assigned to glycoproteins. This is of crucial interest, since glycoproteins make up the majority of allergenic substances in pollen. A classification approach based on this molecular information is introduced. FTIR spectroscopy of single pollen grains for taxonomic classification has not been possible so far, since the pure absorption spectra are superimposed by scattering-based artifacts that change the characteristic fingerprints. Here, it is shown that this problem can be overcome using mathematical tools for data processing. Since pollen grains are highly complex biological particles that consist of many different biomolecules, the data from the different, complementary approaches should be combined. Classification, exploiting as much molecular information as possible is accomplished by the application of the multiblock method Consensus Principal Component Analysis (CPCA). In addition to the application for taxonomic classification, it is conceivable that spectroscopic and spectrometric data could also be used to investigate chemical changes caused by environmental influences. As a first example for such an application, cherry leaf roll virus (CLRV) infected birch pollen are investigated and compared to samples from control plants. The results in this work indicate that spectroscopic and spectrometric methods are powerful analytical tools that may be useful for improved pollen investigation in different areas of research. |
| File Format | PDF HTM / HTML |
| DOI | 10.18452/17483 |
| Alternate Webpage(s) | https://edoc.hu-berlin.de/bitstream/handle/18452/18135/seifert.pdf |
| Alternate Webpage(s) | https://doi.org/10.18452/17483 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
