Key Lipid Oxidation Products Can Be Used to Predict Sensory Quality of Fish Oils with Different Levels of EPA and DHA
Bibliographic record
Abstract
Despite its many health benefits, many consumers avoid fish oil supplements due to fishy tastes and odors. Common chemical measures of oxidation have little correlation with sensory properties, making it difficult to determine the sensory quality of fish oil without the use of an expensive sensory panel. Here we investigate an alternative method to assess oxidation using solid phase microextraction and gas chromatography-mass spectrometry. Fish oils containing different amounts of eicosapentaenoic acid and docosahexaenoic acid were oxidized, and headspace volatiles were monitored over time and compared to sensory evaluations by a taste panel. Peroxide value and anisidine value were also measured. Sensory panel scores and headspace volatile data were analyzed using principal component analysis and linear regression to identify key volatiles responsible for changes in sensory degradation of oils over time. A total of eight compounds were identified, primarily aldehydes and ketones. By monitoring these volatiles, it may be possible to create a simple method to assess oxidation in fish oils that correlates well with sensory properties of the oil without the use of a sensory panel.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".