Enhancing and automating TOF-SIMS data interpretation using principal component analysis

Multivariate tools based on principal component analysis (PCA) have been developed to supplement the usual serial interpretive approach to TOF-SIMS data. The tools are designed to streamline application of PCA so it can be used routinely in a high throughput industrial surface analysis laboratory. Data pretreatment features such as weighting functions, and post-treatment features such as confidence ellipses on scores cluster plots, have been implemented. PCA allows rapid assessment of differences between spectra and can assist in decision-making for common univariate interpretive tasks such as peak integration. PCA is particularly powerful when applied to so-called "raw" data sets, in which a complete mass spectrum is collected to]every pixel in the analysis area. A graphical user interface has been developed that uses PCA to simplify and automate many interpretive functions, such as finding features within SIMS images, selecting region-of-interest spectra from image data, and selecting and displaying the most significant ions in a raw data set. Image interpretation can sometimes be improved by using PCA to reduce topographic effects. In some cases spectral comparisons can be improved through extraction of sub-spectra from raw files, followed by PCA of the sub-spectra. (C) 2004 Elsevier B.V. All rights reserved.