A novel <sup>1</sup>H NMR spectroscopic method for determination of glycidyl fatty acid esters coexisting with acylglycerols
Bibliographic record
Abstract
A new approach for determination of glycidyl fatty acid esters (GDEs) based on 1H nuclear magnetic resonance (1H NMR) spectroscopy was developed. The 1H NMR spectra of the GDE standards were prepared and characterized. The quantification formula was then deduced from the characteristic signals of two epoxy methylene (CH2) protons at chemical shifts 2.56 and 2.76 ppm. Tests on repeatability, reproducibility, and recovery were conducted and proved the reliability of the quantification method. This method was extended to GDEs mixed with oil matrices composed of different acylglycerols (GREs). The weighted average factor fw is introduced to calculate the fatty acid composition based on the stoichiometric proportion of the lipid component and its esterification degree. With the fatty acid composition determined, the molar percentage obtained from 1H NMR can be converted to the weight percentage. This method is an analytical model for the determination of GDEs, with advantages such as easy operation and high accuracy. Practical applications: Heat treatment of edible oils in the refinery process can generate GDEs, a group of contaminants having potential risks to liberate a carcinogenic compound called glycidol during digestion of oil in the human body. It has become an important issue for the oil industry to develop an effective method for the determination of GDEs. The proposed 1H NMR spectroscopic method is able to characterize and quantify GDEs in an accurate, rapid, and noninvasive way. With these advantages, the analytical results also provide quantitative criteria for subsequent concentration and mitigation of GDEs in refined edible oils. A novel 1H NMR spectroscopic method was developed to determine glycidyl fatty acid esters (GDEs), a processing contaminant in refined edible oil.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".