Effect of Chemical Randomization on Positional Distribution and Stability of Omega-3 Oil Triacylglycerols
Bibliographic record
Abstract
Randomization has been commonly used to modify the chemical and physical properties of natural fats and oils. In this study, seal blubber oil (SBO) and menhaden oil (MHO) were modified through chemical randomization using sodium methoxide, and the effect on positional distribution of fatty acids was investigated using gas chromatography (GC) and (13)C nuclear magnetic resonance (NMR) spectroscopy. The effect of randomization on the stability of the original oils and their randomized counterparts was analyzed by comparing conjugated dienes and thiobarbituric acid reactive substances (TBARS) values after accelerated oxidation at 60 degrees C for 4 days. The omega-3 polyunsaturated fatty acids (PUFA) were distributed more evenly among the terminal sn-1,3 positions and the middle sn-2 position in chemically randomized oils when compared to the starting oils. The effect was more pronounced for SBO with omega-3 PUFA attached preferentially to sn-1,3 positions of triacylglycerols before randomization, and it was less pronounced for MHO, which contained omega-3 PUFA more evenly distributed before randomization. However, different levels of commonly known omega-3 fatty acids, namely, docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), eicosapentaenoic acid (EPA), and stearidonic acid (STA), were obtained in both original and randomized oils from GC and (13)C NMR spectroscopy. The stability of the randomized oils was also affected to different degrees, depending on the storage time.
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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 | 0.000 |
| 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".