Tautomerization and Isomerization in Quantitative NMR: A Case Study with 4-Deoxynivalenol (DON)
Bibliographic record
Abstract
The regulated mycotoxin 4-deoxynivalenol (DON) has a heterocyclic structure that is readily amenable to tautomerization and conformational isomerization in solution. An analysis of DON in solution by NMR revealed the presence of hemiacetal tautomer(s) and putative conformational isomers, which maintain the intact enone functional group. The extent and type of tautomerization/isomerization vary according to the NMR solvent used and produce different signal patterns in the NMR spectra. Thus, the same proton produces multiple signals depending on which isomer/tautomer it belongs to. To maintain the accuracy of quantitative NMR (qNMR) measurements, it was essential to conclusively identify all signals belonging to the same proton to avoid underestimating its integral value. A strategy to overcome the complications of DON tautomerization and isomerization in solution during qNMR is reported. Of all proton atoms on the DON carbo-skeleton, H-10 produced clearly defined signals centered at 6.6 ppm for suspected conformational isomers and at 5.5 ppm for hemiacetal tautomers. To determine the purity of DON by quantitative proton NMR, the collective integrals of all isomeric and tautomeric signals belonging to H-10 provided the most accurate value. The purity of DON obtained with this protocol is highly accurate and suitable for the value assignment of certified reference materials (CRMs).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".