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Multivariate analysis of MALDI imaging mass spectrometry data of mixtures of single pollen grains
(2018)
Here, we present an advanced approach to identify pollen grains in mixtures based on:
- a simplified sample preparation procedure
- MALDI imaging mass spectrometry
- chemometric tools
Our goals are to:
- explore the biomolecular variations in pollen
- determine species-specific peak patterns
- discriminate and identify single pollen grains in mixtures using MSI
Das Ziel dieses Projektes ist die Entwicklung neuer Materialien, um den Rückhalt von Mikroplastik-Partikeln (MP) aus verschiedenen urbanen Eintragspfaden (Kläranlagenablauf, Mischwasserüberlauf und Straßenabfluss)zu optimieren.
Die im Rahmen des Projekts entwickelten Materialien sollten auf ihre Rückhaltefähigkeit von MP aus dem Kläranlagenklarlauf getestet werden. Für die Probenahme sowie für die Analytik wurden die entwickelten und optimierten Probenahme- sowie Analysetechniken angewendet.
Wood treated with nano metal fluorides is found to resist fungal decay. Sol−gel synthesis was used to synthesize MgF2 and CaF2 nanoparticles. Electron microscopy images confirmed the localization of MgF2 and CaF2 nanoparticles in wood. Efficacy of nano metal fluoride-treated wood was tested against brown-rot fungi Coniophora puteana and Rhodonia placenta. Untreated wood specimens had higher
mass losses (∼30%) compared to treated specimens, which had average mass loss of 2% against C. puteana and 14% against R. placenta, respectively. Nano metal fluorides provide a viable alternative to current wood preservatives.
The common characterization and identification of pollen is a time-consuming task that mainly relies on microscopic determination of the genus-specific pollen morphology. A variety of spectroscopic and spectrometric approaches have been proposed to develop a fast and reliable pollen identification using specific molecular information. Amongst them, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) shows a high potential for the successful investigation of such complex biological samples. Based on optimized MALDI sample preparation using conductive carbon tape, the application of multivariate statistics (e.g. principal components analysis, PCA) yields an enormous improvement concerning taxonomic classification of pollen species compared to common microscopic techniques.
Since multivariate evaluation of the recorded mass spectra is of vital importance for classification, it’s helpful to implement the applied sequence of standard Matlab functions into a graphical user interface (GUI). In this presentation, a stand-alone application (GUI) is shown, which provides multiple functions to perform fast multivariate analysis on multiple datasets. The use of a GUI enables a first overview on the measured dataset, conducts spectral pretreatment and can give classification information based on HCA and PCA evaluation. Moreover, it can be used to improve fast spectral classification and supports the development of a simple routine method to identify pollen based on mass spectrometry.