Evaluation and optimization of functional and antinutritional properties of aquafaba
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 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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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 | 0.001 | 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) | 0.000 | 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