Nuclear Magnetic Resonance Spectroscopy: A Versatile Tool for Qualitative and Quantitative Analysis of an Emulsifier Mixture of Soybean Oil
Bibliographic record
Abstract
Abstract Finding a fast, reliable, and reproducible approach for an accurate analysis of complex lipid mixtures of emulsifiers is crucial for the food and beverages, pharmaceuticals, personal care products, cosmetics, and agrochemicals industries. In the current study, a comprehensive qualitative and quantitative nuclear magnetic resonance (NMR) spectroscopy analysis of a high monoester mixture of soybean oil (HMMS) was conducted using1H,13C, and31P NMR of 2‐chloro‐4,4,5,5‐tetramethyl‐1,3,2‐dioxaphospholane (CTDP) derivatives. The HMMS was produced by enzymatic alcoholysis of soybean oil and 1.2‐propanediol in a supercritical CO2system. Compositional distribution analysis, quantified by aliphatic carbons with13C NMR, showed that HMMS is composed of more unsaturated fatty acids, comprised of polyunsaturated fatty acids (PUFA) (60 ± 1.1%) and monounsaturated fatty acids (MUFA) (22 ± 0.8%), than saturated fatty acids (18 ± 0.9%). The31P NMR quantification of HMMS demonstrated that, out of the total amount of monoacylglycerols (MAG), they are composed of 21 ± 2.9% of 2‐MAG and 4 ± 0.3% of 1‐MAG. Among the three techniques,31P NMR spectroscopy proved to be a practical methodology with high reproducibility for the precise detection and quantification of partially esterified glycerols and free fatty acids in complex lipid mixtures.
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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 | 0.001 | 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".