Free Radicals Identification from the Complex EPR Signals by Applying Higher Order Statistics

Abstract

EPR spin-trapping technique, using spin-trap DEPMPO, has been shown to be capable of simultaneous detection of multiple free radical species which are generated in the same system. However, such approach proved to be unsuitable due to the complexity of the obtained composite EPR signal of the spin-adducts. Although rather unique, each individual spin-adduct signal is composed of at least eight EPR peaks, thus many of them could be overlapped, making the signal separation process almost impossible to accomplish by using ordinary chemometrics methods such as fast independent component analysis (FastICA), factor analysis (FA), or parallel factor analysis (PARAFAC). We have proposed a new approach which involves cumulative usage of two different statistical techniques. Applied algorithms are based on the second order statistics, second order blind identification with the robust orthogonalization algorithm (SOBI-RO), and the constrained independent component analysis (CICA).