Flavors' Decreasing Contribution to <scp><i>p</i>‐Anisidine</scp> Value over Shelf Life May Invalidate the Current Recommended Protocol for Flavored Fish Oils
Bibliographic record
Abstract
Abstract The American Oil Chemists' Society (AOCS) Official Method Cd 18–90 for p‐Anisidine Value (pAV) is commonly used to evaluate secondary oxidation in fish oils. Flavoring agents in fish oil products may interfere with pAV and lead to inaccurate results. The Global Organization for EPA and DHA (GOED) recommends a protocol for calculating pAV of flavored fish oils based on the assumption that flavors' contribution to the pAV does not change over the course of oxidation. The objective of this study was to test this assumption. All 14 flavors evaluated increased the pAV when added to fresh fish oil; chocolate‐vanilla and lemon flavors generated the largest increase. Under accelerated oxidation conditions, both chocolate‐vanilla and lemon flavors had a similar effect; oxidized flavored fish oils had lower pAV than oxidized fish oils with newly added flavors. This was due to either an antioxidant effect of the flavor or loss of the flavor during oxidation. Following the GOED recommendation, we would have underestimated the oxidation in the flavored oils. For this reason, the pAV of flavored fish oils should be considered with caution and used in combination with other secondary oxidation markers when possible.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".