A comparison of multivariate statistical analysis protocols for ToF-SIMS spectral images

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) instruments produce raw data sets with a tremendous quantity of data. Multivariate statistical analysis (MVA) tools are being used to reduce the massive amount of chemical information into a smaller set of components that are easier to interpret and understand as a result of species association. Standard principal component analysis (PCA) is the most heavily used MVA algorithm in the ToF-SIMS community, and is frequently computed using the singular value decomposition (SVD). Other algorithms such as multivariate curve resolution (MCR) have also gained popularity over the past few years. In this work, we compare the as-measured ToF-SIMS spectrum and ion images with four MVA data analysis protocols: standard PCA, image-rotated SVD, spectra-rotated SVD, and a PCA-based MCR procedure. Image-rotated SVD and spectra-rotated SVD are closely connected to PCA and involve abstract rotations of the singular vectors that naturally arise during computation of the principal components via SVD. These rotations are designed to enhance either spatial contrast or spectral contrast in the components, respectively. We will show that the four MVA protocols provide essentially the same information, but accentuate different aspects of the sample's composition and lateral distribution, and that taken together these methods provide a more complete understanding of the sample. We will demonstrate that the component spectra estimated by MVA protocols assist the analyst in discovering minor constituents and understanding species correlation that would have been difficult, if not impossible, using univariate analysis protocols. For the data set described here, MVA tools identified unexpected species, which were not obvious in the as-measured data. Copyright (C) 2008 John Wiley & Sons, Ltd.