Spatial statistics and interpolation methods for TOF SIMS imaging

Multivariate statistical methods such as principal components analysis (PCA) and factor analysis (FA) have been applied to mass spectral data to extract higher quality information from ion intensities in the mass spectrum. This often leads to better image quality in the resulting image analysis of principal components or factors. This paper presents a second multivariate statistical approach by examining the spatial statistics of the two dimensional image data. Geographic information is analyzed using two and three dimensional spatial statistical methods focused on interpolating spatial distributions. Methods such as Kriging and inverse squared distance weighting are often used to develop spatial distributions of common surface features distributed over geographic distances of meters, kilometers, miles, etc. Geospatial statistics have not been widely applied to spatial chemical distributions of microscopic dimensions. In this paper, we compare ordinary Kriging and inverse squared distance weighting for the analysis of ToF SIMS image data. By selectively eliminating pixels from the original image, we evaluate the accuracy of images reconstructed from 50 to 0.5% of the original dataset. Accurate image reconstruction from small datasets can provide added speed to ToF SIMS image collection and analysis, a potential advantage for on-line ToF SIMS analysis