Destabilization of Egg Yolk Emulsion After IgY Removal Through Enzymatic Treatments
Bibliographic record
Abstract
Abstract The objective of this study was to destabilize the protein–lipid complex in egg yolk precipitate obtained after the removal of soluble proteins, referred to as the pellet, through enzymatic treatment for further phospholipids extraction. A combination of proteolytic and lipolytic enzymes was applied to release the lipids from the pellet or weaken the pellet emulsion. Emulsions prepared using Protease P/Lipase AY30, Protease II/Lipase AY30 and Protease M/Lipase AY30 treated pellets had larger oil droplets (78, 65, 56 µm) and higher coalescence rates (51, 41, 35 %) than those of Protex 51FP, pellet, Protex 7L and Protease A with oil droplet size of 20, 18, 15 and 13 µm and coalescence rates of 31, 8, 7.5 and 8 %, respectively. Cream and liquid subnatant fractions obtained after further centrifugation of hydrolysates were subjected to lipid analyses. Over 90 % of phosphatidylcholine (PC) present in the pellet and 80 % of that in the original egg yolk were recovered in the cream from Protease P/Lipase AY30 treatment, while the recovery of PC from the egg yolk was significantly lower in creams from Protex 7L or Protease 51FP treatments (12 and 10 %, respectively). Pellets treated with Protease M, Protex 7L or Protex 51FP in combination with Lipase AY30 led to a significant loss of PC due to the conversion of PC to lysophosphatidylcholine or its degradation. Cream fractions obtained from the study represented a better material for the recovery of PL than intact egg yolk using environmentally‐friendly techniques such as supercritical carbon dioxide (SC‐CO2) extraction.
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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) | 0.000 | 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 | 0.000 |
| 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".