Blind Separation of Analytes in Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry: Sparseness-Based Robust Multicomponent Analysis

摘要

Metabolic profiling of biological samples involves nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry coupled with powerful statistical tools for complex data analysis. Here, we report a robust, sparseness-based method for the blind separation of analytes from mixtures recorded in spectroscopic and spectrometric measurements. The advantage of the proposed method in comparison to alternative blind decomposition schemes is that it is capable of estimating the number of analytes, their concentrations, and the analytes themselves from available mixtures only. The number of analytes can be less than, equal to, or greater than the number of mixtures. The method is exemplified on blind extraction of four analytes from three mixtures in 2D NMR spectroscopy and five analytes from two mixtures in mass spectrometry. The proposed methodology is of widespread significance for natural products research and the field of metabolic studies, whereupon mixtures represent samples isolated from biological fluids or tissue extracts.