Factors affecting the modification of bovine milk proteins in high hydrostatic pressure processing: An updated review
Bibliographic record
Abstract
High hydrostatic pressure (HHP) treatment induces structural changes in bovine milk proteins depending on factors such as the temperature, pH, concentration, decompression rate, cycling, composition of the medium and pressure level and duration. An in-depth understanding of the impact of these factors is important for controlling HHP-induced modification of milk proteins and the interactions within or between them, which can be applied to prevent undesired aggregation, gelation, and precipitation during HHP processing or to obtain specific milk protein modifications to attain specific protein properties. In this regard, understanding the influences of these factors can provide insight into the modulation and optimization of HHP conditions to attain specific milk protein structures. In recent years, there has been a great research attention on HHP-induced changes in milk proteins influenced by factors such as pH, temperature, concentration, cycling, decompression condition, and medium composition. Hence, to provide insight into how these factors control milk protein structures under HHP treatment and to understand if their effects depend on HHP parameters and environmental conditions, this review discusses recent findings on how various factors (pH, temperature, cycling, decompression rate, medium composition, and concentration) affect HHP-induced bovine milk protein modification. Practical Application: The information provided in this review will be very useful to anticipate the challenges related to the formulation and development of pressure-treated milk and dairy products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".