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Record W4213317690 · doi:10.1021/acs.jafc.1c08053

Tautomerization and Isomerization in Quantitative NMR: A Case Study with 4-Deoxynivalenol (DON)

2022· article· en· W4213317690 on OpenAlexaff
Adilah Bahadoor, Sarah Watt, Isabelle Rajotte, Jennifer Bates

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTautomerIsomerizationChemistryProton NMRKeto–enol tautomerismProtonHemiacetalComputational chemistryStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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