Automated processing of parallel-detection EELS data

Abstract

2014 An algorithm for automatic detection and identification of edges in an EELS spectrum is presented. It has the following features: 1) it compresses the dynamic range of EELS spectra and enhances the ionization edge signals via difference transforms, 2) it removes residual background, thereby isolating sharp features associated with the edge thresholds and noise, 3) it distinguishes true edge-threshold features from noise via statistical analysis. In addition to paving the way for rapid, automated EELS elemental analysis, the algorithm is capable of detecting edges which are easily overlooked by human analysts.