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Unmixing 2D HSQC NMR mixtures with β-NMF and sparsity

Abstract : Nuclear Magnetic Resonance (NMR) spectroscopy is an efficient technique to analyze chemical mixtures in which one acquires spectra of the chemical mixtures along one ore more dimensions. One of the important issues is to efficiently analyze the composition of the mixture, this is a classical Blind Source Separation (BSS) problem. The poor resolution of NMR spectra and their large dimension call for a tailored BSS method. We propose in this paper a new variational formulation for BSS based on a β-divergence data fidelity term combined with sparsity promoting regularization functions. A majorization-minimization strategy is developped to solve the problem and experiments on simulated and real 2D HSQC NMR data illustrate the interest and the effectiveness of the proposed method.
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Contributor : Afef Cherni <>
Submitted on : Thursday, October 29, 2020 - 10:21:17 AM
Last modification on : Saturday, October 31, 2020 - 3:29:32 AM


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Afef Cherni, Sandrine Anthoine, Caroline Chaux. Unmixing 2D HSQC NMR mixtures with β-NMF and sparsity. iTWIST : international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Dec 2020, Nantes, France. ⟨hal-02982896⟩



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