Evaluation and optimization of functional and antinutritional properties of aquafaba
Bibliographic record
Abstract
Abstract Egg protein is responsible for the second most serious of all food allergens, which affects predominantly the children. Therefore, a new type of vegan ingredient called “aquafaba,” is getting recognized as a plant‐based emulsifier in many bakery product preparations instead of the conventionally used egg white and is emerging in the consumer market. It is the residue water from cooked chickpeas. In this study, an I‐optimal mixture experimental design is combined with a response surface methodology to evaluate the chickpeas cooking process for obtaining aquafaba. The following variables were used: chickpea to cooking water ratio (CPCWR; 1:2, 1:4, and 2:3) and cook time (15, 30, 45, and 60 min). The principal goal was to maximize the functional properties and protein content, while minimizing tannin and phytate contents of aquafaba. The results showed that both CPCWR and cooking time had significant effect on the responses. Emulsion properties were the maximum at 2:3 CPCWR and cooking time of 60 min. Foaming capacity was highest (120%) at 2:3 CPCWR cooked for 30 min, whereas the foam was most stable (57 min) at 1:2 CPCWR with 45 min cooking. Water holding capacity reached the maximum level when cooked for 15 min, and oil holding capacity maximum was obtained after 60 min cooking. Polynomial models were developed for all 11 responses. Optimal results were achieved under the following conditions: 1.5:3.5 CPCWR and 60 min cook time, and the overall desirability fraction was 0.81. Validation tests confirmed these results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".