Extracting information from sequences of spatially resolved EELS spectra using multivariate statistical analysis

The sophisticated acquisition procedures now available in spatially or time resolved spectroscopies produce large amounts of data which require efficient automatic data processing procedures. In an electron energy loss spectroscopy (EELS) line-spectrum sequence, all individual spectra can be processed independently. However, it is better to consider the data set as a whole and to process it as such. Multivariate statistical analysis (MSA) can be used to identify several basic sources of information as contributing through a linear combination to the overall experimental data set. In this paper we show that MSA can be applied to spatially resolved spectroscopy, but often necessitates that extension from orthogonal to oblique analysis be performed. The aim is then to identify fine structures characteristic of EELS edges and to extract the signal specific of the interface. In the first section we present the overall methodology. Then it is illustrated through a simulation. Finally, we apply the method to the study of Si–SiO2 and SiO2–TiO2 interfaces. We show that MSA constitutes a useful tool to estimate components present at the interface and the corresponding concentration profile of these components.