Effect of high‐hydrostatic pressure on the digestibility of egg yolk and granule
Bibliographic record
Abstract
The impact of high hydrostatic pressure (HHP) on protein digestibility of egg yolk and egg yolk granule was evaluated by static in vitro digestion using the standardized INFOGEST 2.0 method. The degree of hydrolysis (DH) and the phospholipid content were determined during digestion, and the protein and peptide profiles were characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and reverse phase-high pressure liquid chromatography (RP-HPLC). The results showed that HHP induced protein aggregation in egg yolk and granule, mainly by disulfide bridges, which were not disrupted in the oral phase. Proteolysis during the gastric phase improved egg yolk and granule protein solubility, regardless of whether HHP was applied. However, the extent of the samples' digestibility was not affected, with DH values ranging from 15% to 20%. During the intestinal phase, the DH of egg yolk protein (∼40%) was higher than that of the granule (∼25%), probably due to the denser structure of the granule reducing the accessibility of intestinal enzymes. The DH, peptide, and protein profiles of control and HHP-treated egg yolk showed similar protein digestion behaviors for both gastric and intestinal phases. Among the different proteins, only the digestibility of β-phosvitin in HHP-treated granule was enhanced. Consequently, applying HHP to granules represents an interesting process that improves the digestibility of phosvitin with the potential to generate bioactive phosvitin-derived phosphopeptides. PRACTICAL APPLICATION: High hydrostatic pressure, mainly used as a preservation process, did not impair the nutritional quality of the egg yolk and granule proteins but improved the susceptibility of phosvitin (protein contained in egg yolk) proteolysis to produce bioactive phosphopeptides. Consequently, applying HHP to granules represents an interesting process that improves the digestibility of phosvitin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".