Abstract
Time of flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface-sensitive characterization tool allowing the imaging of chemical properties over a wide range of organic and inorganic material systems. This technique allows precise studies of chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF-SIMS results is challenging because of the very large data volume and high dimensionality. Furthermore, investigation of samples with pronounced topographical features is complicated by systematic and measureable shifts in the mass spectrum. In this work we developed an approach for the interpretation of the ToF-SIMS data, based on the advanced data analytics. Along with characterization of the chemical composition, our approach allows extraction of the sample surface morphology from a time of flight registration technique. This approach allows one to perform correlated investigations of surface morphology, biological function, and chemical composition of Arabidopsis roots.
Highlights
ToF-SIMS data interpretation can be challenging, as acquired datasets are multidimensional, with multiple mass spectra measured along 2 or 3-dimensional grids
We develop an approach for comprehensive interpretation of multidimensional ToF-SIMS data by utilizing multivariate statistical analysis and applying this approach to a challenging problem in biological imaging
In this work we developed an automated approach to comprehensively interpret multidimensional mass spectrometry data based on multivariate analysis
Summary
The total number of spectra can exceed 1 million for modern ToF-SIMS instruments with each spectrum consisting of multiple peaks, corresponding to a certain element or a molecular cluster These multidimensional datasets are often collected from samples having pronounced topographical features, which can introduce significant peak shifts that further confound automated data analysis[16,17,18]. The sheer number of spectral points in the raw mass spectrum, makes performing MVA of ToF-SIMS computationally intensive To overcome this issue, coarse data pre-processing by binning (with bin width up to 1 u) or analysis of the preselected peaks of interest can ameliorate the processing load. This universal approach allows robust shift correction, and enables qualitative topography reconstruction solely from the ToF-SIMS data
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