Impact of high hydrostatic pressure on casein micelle‐pea protein systems and comparison with heat treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it