The Problem of NMR Data Analysis in Synthetic Chemistry: Why Is It Important to Understand Spectra?
- 1Independent Researcher
Abstract
High-resolution NMR (nuclear magnetic resonance) is one of the key methods for establishing the structures of organic compounds. Modern instrumentation and reference data provide a sufficient range of experiments for unambiguous assignment. However, in practice these resources are applied selectively. An assignment is commonly treated as complete once it no longer contradicts the expected structure. It is complete only when the spectrum as a whole has been explained, including signals at odds with that structure. The discussion is built on six papers retracted between 2020 and 2025, in which the reported structure was incorrect and the original assignment rested on NMR data. These cases are not used to estimate how often such errors occur, but to illustrate types of error that keep recurring in the literature. The same errors are then shown in a paper where the structure is correct and firmly supported by orthogonal methods. Two criteria are proposed for assessing a reported spectrum without access to the raw data. The first is the resolution indicated by the multiplet of the residual solvent signal. Therefore, this multiplet should remain visible in spectra reproduced in the Supporting Information. The second is the internal consistency of the reported data. The stated multiplicity must match the number of coupling constants. The notation must also make sense for the structure: simple descriptions such as “d” or “t” are not valid, for example, for the signals of a monosubstituted or unsymmetrically 1,4-disubstituted benzene ring. Automated approaches inherit rather than remove this difficulty: their output is accepted with the same confidence.
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BibTeX
@misc{eshtukovshcheglov2026problem,
title = {{The Problem of NMR Data Analysis in Synthetic Chemistry: Why Is It Important to Understand Spectra?}},
author = {Artur V. Eshtukov-Shcheglov},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010060/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010060/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - The Problem of NMR Data Analysis in Synthetic Chemistry: Why Is It Important to Understand Spectra?
AU - Artur V. Eshtukov-Shcheglov
PY - 2026
DA - 2026/10/07
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010060/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010060/v1
AB - High-resolution NMR (nuclear magnetic resonance) is one of the key methods for establishing the structures of organic compounds. Modern instrumentation and reference data provide a sufficient range of experiments for unambiguous assignment. However, in practice these resources are applied selectively. An assignment is commonly treated as complete once it no longer contradicts the expected structure. It is complete only when the spectrum as a whole has been explained, including signals at odds with that structure. The discussion is built on six papers retracted between 2020 and 2025, in which the reported structure was incorrect and the original assignment rested on NMR data. These cases are not used to estimate how often such errors occur, but to illustrate types of error that keep recurring in the literature. The same errors are then shown in a paper where the structure is correct and firmly supported by orthogonal methods. Two criteria are proposed for assessing a reported spectrum without access to the raw data. The first is the resolution indicated by the multiplet of the residual solvent signal. Therefore, this multiplet should remain visible in spectra reproduced in the Supporting Information. The second is the internal consistency of the reported data. The stated multiplicity must match the number of coupling constants. The notation must also make sense for the structure: simple descriptions such as “d” or “t” are not valid, for example, for the signals of a monosubstituted or unsymmetrically 1,4-disubstituted benzene ring. Automated approaches inherit rather than remove this difficulty: their output is accepted with the same confidence.
ER -