Evaluation of Changes in Protein Quality of High-Pressure Treated Aqueous Aquafaba
Bibliographic record
Abstract
Chickpea cooking water (CCW), known as aquafaba, has potential as a replacement for egg whites due to its emulsion and foaming properties which come from the proteins and starch that leach out from chickpeas into the cooking water. High pressure (HP) processing has the ability to modify the functional characteristics of proteins. It is hypothesized that HP processing could favorably affect the functional properties of CCW proteins by influencing their structure. The objective of this study to evaluate the effect of HP treatment on the associated secondary structure, emulsion properties and thermal characteristics of CCW proteins. A central composite rotatable design is used with pressure level (227–573 MPa) and treatment time (6–24 min) as HP variables, and concentration of freeze dried CCW aquafaba powder (11–29%) as product variable, and compared to untreated CCW powder. HP improves aquafaba emulsion properties compared to control sample. HP reduces protein aggregates by 33.3%, while β-sheets decreases by 4.2–87.6% in which both correlated to increasing protein digestibility. α-helices drops by 50%. It affects the intensity of some HP treated samples, but not the trend of bands in most of them. HP treatment decreases Td and enthalpy because of increasing the degree of denaturation.
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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".