Multivariate statistical analysis of a series of alchemi spectra

Abstract

Multivariate statistical analysis (MSA) of a series of spectra or images offers an objective and quantitative way to characterize the features of the spectra that vary in a correlated fashion and to determine the number of independently varying components in the series. For example, in a series of spectra showing grain boundary segregation, there may be only one independently varying spectral component, which signifies an increase in the concentrations of the segregants and a corresponding decrease in the concentrations of some of the matrix constituents. The basis of the MSA method has been outlined by Trebbia and Bonnet, with application to the analysis of electron energy-loss spectrum images. Titchmarsh et al., have applied this analysis to a series of energy dispersive X-ray (EDX) spectra for the study of grain boundary segregation. The present paper illustrates the application of MSA methods to a series of EDX spectra acquired for ALCHEMI analysis. The basic method has been modified slightly for the analysis of ALCHEMI data.