Multivariate image analysis methods applied to XPS imaging data sets

Recent improvements in imaging photoelectron spectroscopy enhance lateral and vertical characterization of heterogeneous samples at the cost of increasing complexity in the XPS data sets acquired. These imaging data sets require more sophisticated analysis methods than visual inspection if the data are to be interpreted effectively. Multivariate analysis (MVA) methods are increasingly utilized in surface spectroscopies to aid the analyst in interpreting the vast amount of information resulting from these multidimensional data set acquisitions. In this work, image processing analysis methods are tested on XPS data sets acquired from polymer blends. Images from the blends, acquired as a function of composition, time or energy, provide multidimensional data sets for algorithm evaluation. Multivariate image analysis (MIA) methods such as scatter diagrams, principal component analysis (PCA) and classification methods are used to extract maps of pure components from degradation and images-to-spectra data sets. In some cases the MVA results can be compared directly with the XPS spectra or images, which provide a critical reference point. This work will demonstrate that additional information can result from the application of MIA methods, even when direct spectral or image interpretation is possible. Copyright (C) 2002 John Wiley Sons, Ltd.