The Use of Coupled HSQC Spectra to Aid in Stereochemical Assignments of Molecules with Severe Proton Spectral Overlap
Bibliographic record
Abstract
INTRODUCTION: A simple glycoside with only 13 carbons exhibited extensive overlapping of four of the glycosidic protons, causing extreme difficulty in the determination of the stereochemistry of the pyranose unit. However, acquisition of a high-resolution coupled heteronuclear single-quantum coherence (HSQC) spectrum overcame this problem. This spectrum provides a useful method for determining vicinal proton coupling constants between strongly coupled protons. OBJECTIVE: To show the potential of high-resolution coupled HSQC spectra in overcoming spectral overlap. METHODOLOGY: The sample was obtained by methanol extraction, followed by fractionation and column chromatography of the dried leaves of Montrichardia arborescens (Araceae). NMR spectra were obtained on 1.5 mg of sample dissolved in 120 μL of CD₃OD containing 0.1% trimethylsilyl (TMS) as internal standard. A gradient-selected HSQC spectrum was obtained using standard Varian library pulse sequences in phase sensitive mode. The high-resolution coupled HSQC spectrum focused on the saccharide region with a 1025 Hz ¹H spectral window, a 6300 Hz ¹³C spectral window, 1024 data points, a 0.3 Hz relaxation delay, 384 time increments (linear predicted to 4096), and 80 scans per time increment. RESULTS: The use of a high-resolution coupled HSQC spectrum allowed determination of the coupling patterns of the various pyranose protons with sufficient accuracy. This enabled completion of the assignments and identification of the pyranose unit as glucose. CONCLUSION: The study has shown the effectiveness of the use of a high-resolution coupled HSQC spectrum in the assignment of molecules with severe spectral overlap.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".