Effect of Freezing, Thermal Pasteurization, and Hydrostatic Pressure on Fractionation and Folate Recovery in Egg Yolk
Bibliographic record
Abstract
In this study, the impact of pasteurization and freezing of raw material, as performed at a commercial scale, on egg yolk fractionation and folate recovery was assessed. Freezing induced denaturation of the lipoproteins in egg yolk, which prevented further fractionation of the yolk. Thermal pasteurization of egg yolk at 61.1 °C for 3.5 min as well as high hydrostatic pressure (HHP) treatment (400 MPa for 5 min) did not change (p < 0.05) the composition of egg yolk or yolk fractions after their recovery by centrifugation. Expressed as dry matter, folate in pasteurized yolk was measured to be 599 μg/100 g, while its concentration reached 1969.7 μg/100 g for pasteurized granule and 1902.5 μg/100 g for HHP-treated granule. Folate was not detected in plasma, emphasizing the complete separation of yolk folate into granule. Further, we studied the effect of HHP on different dilutions of egg yolk, which were then fractionated. Egg yolk was diluted with water at different concentrations (0.1, 1.0, 10, 25, and 50%), HHP-treated at 400 MPa for 5 min, and centrifuged. Characterization of the compositions of the separated granule and plasma followed. Folate was stable under the HHP conditions used. However, HHP caused separation of folate from the yolk structure into water-soluble plasma. After HHP processing, the amount of folate detected in the plasma fraction was significantly (p < 0.05) higher (1434.9 μg/100 g) in the 25% diluted samples but was significantly (p < 0.05) lower in HHP-treated granule samples. Native sodium dodecyl sulfate-polyacrylamide gel electrophoresis results showed that phosvitin, α-livetin, and apovitellenin VIa were the proteins most resistant to HHP. This study confirms that dilution of egg yolk before HHP treatment can significantly (p < 0.05) change the composition of granule and plasma fractions after centrifugal fractionation of egg yolk.
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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 | 0.001 |
| 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".