MEASURING OXIDATIVE STABILITY OF STRUCTURED LIPIDS BY PROTON NUCLEAR MAGNETIC RESONANCE
Bibliographic record
Abstract
ABSTRACT The oxidative stability of enzymatically modified oils (structured lipids) and their unmodified counterparts were assessed using proton nuclear magnetic resonance (1H NMR) spectroscopy. This methodology was used to monitor relative changes in the proton absorption pattern of the fatty acids of oils during storage at 60C. Relative changes of aliphatic to olefinic (Rao) and aliphatic to diallylmethylene (Rad) proton ratios during oil oxidation were determined by 1H NMR spectroscopy. An increase in Rao and Rad values was obtained over the entire storage period. The oxidative stability of oils was also evaluated using conjugated dienes (CD) determination, 2‐thiobarbituric acid reactive substances (TBARS) and headspace volatile analysis. A highly significant correlation (r = 0.930–0.992; P ≤ 0.005) existed between the CD values and changes in Rao and Rad during oxidation of all oils. The correlation coefficient between TBARS and changes in Rao and Rad values was in the range of 0.779–0.983 (P ≤ 0.05). A high correlation (r = 0.948–0.996; P ≤ 0.005) was found between hexanal content and Rao and Rad of oils. Propanal content was also highly correlated (r = 0.950–0.990; P ≤ 0.005) with Rao and Rad. PRACTICAL APPLICATIONS Assessment of the extent of lipid oxidation in food is of much interest to producers and scientists alike. Proton nuclear magnetic resonance spectroscopy provides a valuable tool for quantitation of oxidation of food lipids. This procedure was used for evaluating the oxidative state of structured lipids. The procedure is rapid and nondestructive, requires a small amount of material, and may be considered as “green” because it uses a very minimum amount of solvent and is readily applicable to edible oils and oils extracted from food and biological samples.
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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 | 0.000 |
| 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 | 0.000 |
| 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".