Thermal processing–driven functional transformations of egg components: Albumen and yolk
Bibliographic record
Abstract
Eggs are globally valued for their nutritional density, versatility, and accessibility, serving as key ingredients in both domestic and industrial food systems. Thermal processing, which is essential for microbial safety and shelf life, induces complex structural, functional, and nutritional changes in egg components. This review critically synthesizes evidence on the thermal behavior of albumen and yolk, linking protein denaturation, lipoprotein aggregation, and rheological shifts to digestibility, bioactive peptide release, and lipid bio-accessibility. The study identified a narrow optimal thermal window (62-68 °C for yolk and 75-85 °C for egg-white gels) that balances microbial safety with functional integrity. This range maximizes emulsification stability and proteolysis while preventing excessive aggregation that could reduce bioaccessibility. Thermal transitions are matrix- and rate-dependent, with ovalbumin denaturation shifting according to protein concentration, ionic strength, and heating rate. This highlights the importance of reporting onset, peak, and endset temperatures together with relevant process parameters. Evidence suggests that thermal gelation reinforces protein networks, reducing free fatty acid release at ≥85 °C, indicating a mechanistic link between matrix densification and diminished lipid digestion. Notably, moderate short-duration heating combined with non-thermal or fortification strategies can enhance peptide yield, antioxidant activity, and micronutrient retention, supporting the development of functional egg products. In conclusion, optimal egg processing should be regarded as a multi-objective optimization challenge that integrates microbial safety, structure-function performance, and nutritional quality. Future research should prioritize standardized pre-processing protocols, mechanistic modeling of denaturation-aggregation kinetics, and thermal characterization of recombinant egg proteins to enable predictive and precision-based process design. Review outline.
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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 | 0.001 |
| 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".