Gaussian apodization factor analysis for resolution of the embedded peaks in real complicated chromatographic datasets

Gaussian apodization factor analysis (GAFA) has been developed as an enhanced algorithm to assess the peak purity of the two-dimensional data, by weighting the fixed-size moving window via Gaussian formula. In GAFA method, submatrices are extracted by Gaussian apodization moving window. Therefore, each submatrix mainly characterizes the spectrum and by performing factor analysis on this Gaussian weighted submatrix, the number of principal components for each evaluated spectrum, is determined. This precise and quick determination of a rank map is successfully used for extract pure components from hyphenated chromatographic data. An algorithm based on GAFA was applied to resolve different types of overlapped simulated and real complex data of GC-MS. This algorithm finds spectra of pure component with GAFA one by one and eliminates obtained components from a data matrix and search for next pure component spectra until all the components are determined.