Impact of high hydrostatic pressure on casein micelle‐pea protein systems and comparison with heat treatment
Bibliographic record
Abstract
Abstract Developing mixed systems with both plant‐ and animal‐based proteins is crucial to address the limitations in the techno‐functional properties of plant‐based proteins. While the impact of thermal co‐aggregation on mixed systems has been extensively studied, there is limited information on the effects of non‐thermal processes. Therefore, this study aimed to compare the effects of high hydrostatic pressure (HHP, 600 MPa–5 min) and heat (90°C for 60 min) treatments on the protein profiles in a mixed micellar casein (CN):pea protein (PPI) system, while also elucidating the interactions involved in the formation of protein aggregates. Our results showed that both HHP and heat treatments induced the formation of soluble protein aggregates through disulfide bonds. However, protein aggregation was less prominent after application of HHP. In both treatments, the aggregates primarily consisted of convicilin, vicilin, legumin and lipoxygenase. However, albumin PA2 did not contribute to HHP‐induced aggregates, and vicilin played a lesser role in their formation compared to heat‐induced aggregates. CN from the HHP‐treated CN:PPI sample did not participate in aggregate formation, as previously demonstrated after heat treatment. The presence of residual whey proteins in the CN ingredients explained the formation of CN‐whey protein aggregates after heat treatment and, to a lesser extent, after HHP treatment.
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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".