Encapsulation of Long‐Chain <i>n</i>‐3 Polyunsaturated Fatty Acids Using Egg Yolk
Bibliographic record
Abstract
Abstract Egg yolk is well known for its excellent emulsifying property. In this article, egg yolk was used as the encapsulating matrix to prevent the oxidation of n‐3 long‐chain polyunsaturated fatty acids from fish oil. A 2 × 2 × 5 complete block design with three replications was used. Two levels of fish oil (1% and 5%) and two levels of esterification type (triglycerides or ethyl esters) of eicosapentaenoic/docosahexaenoic fatty acids were used. Time was considered a fixed factor with five levels. Emulsions were prepared by homogenization and stored for up to 4 weeks at 4–6 °C, with weekly sampling. Emulsions were analyzed for particle size and distribution, encapsulation efficiency, and surface oil. The oxidative stability of the emulsions was evaluated before and after cooking at 150–170 °C for 75 s. The addition of triglycerides resulted in a larger average particle size (234 ± 12.4 nm). All emulsions achieved 100% encapsulation efficiency and showed no significant change in the surface oil concentration during storage. After 4 weeks of storage, the concentration of eicosapentaenoic + docosahexaenoic fatty acids in nonencapsulated fish oil triglycerides and ethyl esters decreased by 20.32% and 14.74%, respectively, while the emulsions showed no significant differences. In addition, no peroxide or propanal formation was detected in raw emulsions over the storage period. Propanal formation was negligible in cooked samples, and the peroxide value showed no differences between the egg yolk control and the emulsions. Therefore, egg yolk was observed to be an efficient encapsulating food matrix that protects n‐3 polyunsaturated fatty acids against oxidation and degradation.
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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 | 0.000 |
| 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) | 0.000 | 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".