Division of Laser and Atomic Research and Development, Ruder Bosković Institute, Bijenicka cesta 54, HR-10000, Zagreb, Croatia.
Anal Chem. 2010 Mar 1;82(5):1911-20. doi: 10.1021/ac902640y.
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.
生物样本的代谢轮廓分析涉及到与强大的统计工具相结合的核磁共振（NMR）光谱和质谱，用于复杂数据分析。在这里，我们报告了一种稳健的、基于稀疏性的方法，用于从光谱和光谱测量中记录的混合物中分离分析物。与替代的盲目分解方案相比，该方法的优势在于，它仅能够从可用的混合物中估计分析物的数量、它们的浓度以及分析物本身。分析物的数量可以小于、等于或大于混合物的数量。该方法在二维 NMR 光谱中从三个混合物中盲提取四个分析物和在质谱中从两个混合物中盲提取五个分析物的实例中得到了说明。所提出的方法对于天然产物研究和代谢研究领域具有广泛的意义，其中混合物代表从生物流体或组织提取物中分离的样品。
