Modeling the Primary Oxidation in Commercial Fish Oil Preparations
Bibliographic record
Abstract
The quality of commercial fish oil products can be difficult to maintain because of the rapid lipid oxidation attributable to the high number of polyunsaturated fatty acids (PUFA), specifically eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). While it is known that oxidation in fish oil is generally the result of a direct interaction with oxygen and fatty acid radicals, there are very few studies that investigate the oxidation kinetics of fish oil supplements. This study uses hydroperoxides, a primary oxidation product, to model the oxidation kinetics of two commercially available fish oil supplements with different EPA and DHA contents. Pseudo first order kinetics were assumed, and rate constants were determined for temperatures between 4 and 60 °C. This data was fit to the Arrhenius model, and activation energies (E(a)) were determined for each sample. Both E(a) agreed with values found in the literature, with the lower PUFA sample having a lower E(a). The oil with a lower PUFA content fit the first-order kinetics model at temperatures ≥20 °C and ≤40 °C, while the higher PUFA oil demonstrated first-order kinetics at temperatures ≥4 °C and ≤40 °C. When the temperature was raised to 60 °C, the model no longer applied. This indicates that accelerated testing of fish oil should be conducted at temperatures ≤40 °C.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".