Simplex: A Method for Spectral Deconvolution Applicable to Energy Dispersion Analysis

Abstract

In many types of spectral analysis, the quantitation of results is often complicated by the presence of overlapping peaks. We have treated deconvolution as a parameter estimation problem and have applied an optimization procedure called simplex to resolve the true area of overlapping peaks such as those frequently encountered in energy dispersion analysis spectra. This technique overcomes many of the limitations of present deconvolution techniques such as resolution enhancement and digital filter-correlation and has the added advantage of not involving the use of partial derivatives as encountered in gradient-type optimization techniques. This procedure has been applied to a series of artificially generated overlapping Gaussian peaks which are close analogs to actual peaks. The results of this study have shown that this function converges and the correct area of those overlapping peaks can be estimated by this deconvolution.