TY - CONF A1 - Lauer, Franziska A1 - Diehn, Sabrina A1 - Seifert, Stephan A1 - Kneipp, Janina A1 - Sauerland, V. A1 - Barahona, C. A1 - Weidner, Steffen T1 - Multivariate analysis of MALDI imaging mass spectrometry data of mixtures of single pollen grains N2 - 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 T2 - 66th Conference on Mass Spectrometry and Allied Topics CY - San Diego, CA, USA DA - 03.06.2018 KW - Pollen PY - 2018 AN - OPUS4-45196 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lauer, Franziska A1 - Diehn, Sabrina A1 - Weidner, Steffen A1 - Kneipp, Janina T1 - A graphical user interface for a fast multivariate classification of MALDI-TOF MS data of pollen grains N2 - 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. T2 - 12. Interdisziplinäres Doktorandenseminar, GDCh AK Prozessanalytik CY - BAM, AH, Berlin, Germany DA - 25.03.2018 KW - MALDI KW - GUI KW - Pollen PY - 2018 AN - OPUS4-44661 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lauer, Franziska A1 - Diehn, Sabrina A1 - Seifert, Stephan A1 - Kneipp, Janina A1 - Sauerland, V. A1 - Barahona, C. A1 - Weidner, Steffen T1 - Multivariate analysis of MALDI imaging mass spectrometry data of mixtures of single pollen grains JF - Journal of the American Society for Mass Spectrometry N2 - Mixtures of pollen grains of three different species (Corylus avellana, Alnus cordata, and Pinus sylvestris) were investigated by matrixassisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI-TOF imaging MS). The amount of pollen grains was reduced stepwise from > 10 to single pollen grains. For sample pretreatment, we modified a previously applied approach, where any additional extraction steps were omitted. Our results show that characteristic pollen MALDI mass spectra can be obtained from a single pollen grain, which is the prerequisite for a reliable pollen classification in practical applications. MALDI imaging of laterally resolved pollen grains provides additional information by reducing the complexity of the MS spectra of mixtures, where frequently peak discrimination is observed. Combined with multivariate statistical analyses, such as principal component analysis (PCA), our approach offers the chance for a fast and reliable identification of individual pollen grains by mass spectrometry. KW - MALDI Imaging MS KW - Pollen grains KW - Multivariate Statistics KW - Hierarchical cluster analysis KW - Principal component analysis PY - 2018 DO - https://doi.org/10.1007/s13361-018-2036-5 SN - 1044-0305 SN - 1879-1123 VL - 29 IS - 11 SP - 2237 EP - 2247 PB - Springer AN - OPUS4-45607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -