A demonstration of Uncorrelated Component Analysis for estimating nitric oxide ESR spectra

Abstract

ESR spectroscopy often suffers the problem of spectral overlapping which made the identification and quantification of the spectra of interest very difficult, and this is the case for nitric oxide (NO) detection in animal model. Uncorrelated Component Analysis (UCA) method is demonstrated in this work to estimate two kinds of real-world NO-adduct spectra from their detected spectral mixtures. Results show that relatively “pure” NO-adduct spectra were extracted by UCA. Compared to traditional signal subtraction method with one component must being known, this method requires no previous knowledge of any components, thus is much easier to perform.