[NMR paper] Quantitative Quantum Mechanical Spectral Analysis (qQMSA) of 1H NMR Spectra of Complex Mixtures and Biofluids

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Quantitative Quantum Mechanical Spectral Analysis (qQMSA) of 1H NMR Spectra of Complex Mixtures and Biofluids

Publication date: Available online 18 February 2014
Source:Journal of Magnetic Resonance

Author(s): Mika Tiainen , Pasi Soininen , Reino Laatikainen

The quantitative interpretation of 1H NMR spectra of mixtures like the biofluids is a demanding task due to spectral complexity and overlap. Complications may arise also from water suppression, T2-editing, protein interactions, relaxation differences of the species, experimental artifacts and, furthermore, the spectra may contain unknown components and macromolecular background which cannot be easily separated from baseline. In this work, tools and strategies for quantitative Quantum Mechanical Spectral Analysis (qQMSA) of 1H NMR spectra from complex mixtures were developed and systematically assessed. In the present approach, the signals of well-defined, stoichiometric components are described by a QM model, while the background is described by a multiterm baseline function and the unknown signals using optimizable and adjustable lines, regular multiplets or any spectral structures which can be composed from spectral lines. Any prior knowledge available from the spectrum can also be added to the model. Fitting strategies for weak and strongly overlapping spectral systems were developed and assessed using two basic model systems, the metabolite mixtures without and with macromolecular (serum) background. The analyses show that if the spectra are measured in high-throughput manner, the consistent absolute quantification demands some calibration to compensate the different response factors of the protons and compounds. On the other hand, the results show that also the T2-edited spectra can be measured so that they obey well the QM rules. In general, qQMSA exploits and interprets the spectral information in maximal way taking full advantage from the QM properties of the spectra and, at the same time, offers chemical confidence which means that individual components can be identified with high confidence on the basis of their accurate spectral parameters.
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