Method of and system for blind extraction of more pure components than mixtures in 1D- and 2D-NMR spectroscopy and mass spectrometry by means of combined sparse component analysis and detection of single component points

The present invention generally relates to a computer-implemented system for processing data for the purpose of blind extraction of more pure components than mixtures recorded in the fields of 1Dor 2D-NMR spectroscopy and mass spectrometry. Specifically, the invention is related to the application of the method of sparse component analysis in combination of detection of single component points to blind decomposition of NMR spectroscopy or mass spectrometry data X into pure components S and concentration matrix A, whereas the number of pure components S is greater than number of mixtures X. Spectroscopic data refers to data gathered by 1Dor 2Dnuclear magnetic resonance (NMR) spectroscopy or mass spectrometry. NMR mixtures are transformed into wavelet domain by means of wavelet transform T1, wherein pure components in the wavelet domain are sparser than in the recording domain. By means of direction based criterion single component points (SCPs) of the mixtures in wavelet domain are detected where only one pure component is active. These SCPs are used for estimation of the unknown number of pure components by means of data clustering function and any two out of n 2 mixtures. The same SCPs are also used for estimation of the concentration matrix by means of data clustering methods. The pure components are estimated in frequency domain by means of linear programming, convex programming with quadratic constraint ( 2 -norm based constraint) or quadratic programming method with 1 -norm based constraint. Mass spectrometry mixtures are extended to analytical continuation that is necessary to obtain complex signal required by direction based SCPs detection criterion. Identified SCPs in mass spectrometry mixtures data are used for estimation of the unknown number of pure components by means of data clustering function and any two out of n 2 mixtures. The same SCPs are also used for estimation of the concentration matrix by means of data clustering methods. The pure components mass spectra are estimated in recording m/z domain by means of linear programming, convex programming with quadratic constraint ( 2 -norm based constraint) or quadratic programming method with 1 -norm based constraint. The estimated pure components are ranked using negentropy-based criterion. Components with negentropy measure that differs 10 orders of magnitudes or more from the negentropy of the majority of the pure components are classified as outliers and eliminated.