<i>Cis</i> and <i>trans</i> components of lipids: Analysis by <sup>1</sup>H NMR and silver shift reagents
Bibliographic record
Abstract
Abstract In this study, the methodology of shift reagents was exploited to distinguish cis and trans unsaturation in oils and fats. The differential binding of silver ions (in the form of AgFOD) to cis and trans double bonds allowed the separation of the allylic and olefinic proton signals in the 1H NMR spectra of mixtures of cis and trans methyl esters of monoene aliphatic acids and unsaturated triacylglycerol mixtures at low frequency spectrometers (300 MHz). Careful integration of the appropriate proton resonances in the recorded quantitative 1H NMR spectra afforded percentage concentrations in very good agreement with the actual values. This 1H NMR methodology was validated by analyzing AOCS Laboratory Proficiency Program GC samples containing various percentages of saturated, cis‐mono unsaturated, and cis‐polyunsaturated fat as well as trans content. This fast and relatively low‐cost NMR methodology could be used on line for obtaining nutrition labeling compositional data (NLCD) required for fat‐containing food products. Attempts to differentiate lipid molecules with different degree of unsaturation and positional distribution of cis double bonds were unsuccessful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".