Resolution of mixture components by target transformation factor analysis and determinant analysis for the selection of targets

A combination of target testing and an iterative determinant analysis is incorporated into a mixture analysis method that achieves data decomposition in the spectral space by successive average orthogonalization (SAO) and the estimation of the pure component spectra by iterative target transformation factor analysis (ITTFA). This mixture analysis method starts with the resolution of component spectra. Hence, the selection of targets for further transformation is critical when a component spectrum contains multiple peaks, Various synthesized spectra, as well as IR spectra collected during the formation of a polyurethane foam, have been analyzed, SAO is shown to produce eigenvectors comparable to those obtained by some well-established methods, such as SVD and NIPALS, It is preferred in this research because it allows the processing of a potentially unlimited number of spectra and linearly dependent data sets, The results of the analysis of both the synthesized spectra and the real IR spectra demonstrate that the current method can resolve highly overlapping mixture spectra, producing spectra equivalent to the original components, Because it attempts the resolution in spectral space, there is no requirement about the shapes of the concentration profile, Therefore, it is capable of probing kinetics in complex processes, The determinant analysis with ITTFA is general and can be applied to eigenvectors produced by matrix decomposition methods other than SAO.