Henrik Arlinghaus oral presentation (FN2-Tue1-1-6)
Multivariate Analysis of combined ToF-SIMS and Orbitrap-SIMS data
1 ION-TOF GmbH, Heisenbergstr. 15, 48149 Muenster, Germany
2 , 8346 Roney Rd, NY 14590 Wolcott, United States
Advances in SIMS instrumentation, such as the advent of gas cluster ion sources, have greatly increased the analysis capabilities on organic samples, e.g. by reducing molecular fragmentation. However, the identification of molecules may still be limited by the mass resolution and mass accuracy of the analyzer. A Hybrid SIMS instrument, combining a ToF-SIMS mass analyzer and an OrbitrapTM mass analyzer (Q ExactiveTM HF) has been developed in order to overcome these limitations, combining the high lateral and depth resolution and repetition rate of the ToF-SIMS analyzer with the high mass resolution, mass accuracy, and MS-MS capabilities of the Q Exactive HF analyzer (240,000 @ m/z = 200, sub ppm accuracy). This instrument generates a vast amount of data, rendering manual analysis of the full dataset impractical.
Multivariate analysis (MVA) may be used to reduce complex datasets to a small set of relevant factors, simplifying data interpretation. Established multivariate techniques, such as principal component analysis (PCA), have been used to analyze everything from a small set of inorganic spectra to complex three dimensional organic samples consisting of hundreds of millions of voxel spectra, such as OLEDs. These techniques are now routinely used for ToF-SIMS data analysis in many laboratories.
We will present results of multivariate analysis of datasets acquired using a Hybrid SIMS instrument, where we simultaneously analyzed both the ToF-SIMS and Orbitrap-SIMS data. This type of analysis presents unique challenges, such as contending with vastly different detector technologies and the corresponding differences in noise characteristics.
 Passarelli et al, The 3D OrbiSIMS – A new Method for Label-Free Metabolic Imaging with Sub-cellular Lateral Resolution and High Mass Resolution, submitted 2017.