A Critical Evaluation of the Quality of Published 13C NMR Data in Natural Product Chemistry

Part of the Progress in the Chemistry of Organic Natural Products book series (POGRCHEM, volume 105)


Nuclear Magnetic Resonance spectroscopy contributes very efficiently to the structure elucidation process in organic chemistry. Carbon-13 NMR spectroscopy allows direct insight into the skeleton of organic compounds and therefore plays a central role in the structural assignment of natural products. Despite this important contribution, there is no established and well-accepted workflow protocol utilized during the first steps of interpreting spectroscopic data and converting them into structural fragments and then combining them, by considering the given spectroscopic constraints, into a final proposal of structure. The so-called “combinatorial explosion” in the process of structure generation allows in many cases the generation of reasonable alternatives, which are usually ignored during manual interpretation of the measured data leading ultimately to a large number of structural revisions. Furthermore, even when the determined structure is correct, problems may exist such as assignment errors, ignoring chemical shift values, or assigning lines of impurities to the compound under consideration. An extremely large heterogeneity in the presentation of carbon NMR data can be observed, but, as a result of the efficiency and precision of spectrum prediction, the published data can be analyzed in substantial detail.

This contribution presents a comprehensive analysis of frequently occurring errors with respect to 13C NMR spectroscopic data and proposes a straightforward protocol to eliminate a high percentage of the most obvious errors. The procedure discussed can be integrated readily into the processes of submission and peer-reviewing of manuscripts.


Computer-assisted structure elucidation Structure revision Spectrum prediction Isomer generation Structure generation Nuclear Magnetic Resonance spectroscopy 13C-NMR/Carbon NMR Peer-reviewing Quality of spectroscopic data Signal assignment 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Organic ChemistryUniversity of ViennaViennaAustria

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