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 posttreatment 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 for 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. # 2004 Elsevier B.V. All rights reserved