Aquafaba from Korean Soybean I: A Functional Vegan Food Additive
Bibliographic record
Abstract
The substitution of animal-based foods (meat, eggs, and milk) with plant-based products can increase the global food supply. Recently, pulse cooking water (a.k.a. aquafaba) was described as a cost-effective alternative to the egg in gluten-free, vegan cooking and baking applications. Aquafaba (AQ) forms stable edible foams and emulsions with functional properties that are like those produced by whole egg and egg white. However, the functional ingredients of AQ are usually discarded during food preparation. In this study, Korean-grown soy (ver. Backtae, Seoritae, and Jwinunikong) and chickpea were used to produce AQ. Two approaches were compared. In the first, seed was cooked at an elevated pressure without presoaking. In the second, seed was soaked, then, the soaking water was discarded, and soaked seed was cooked at an elevated pressure. Both approaches produced a useful emulsifier, but the latter, with presoaking, produced a superior product. This approach could lead to a process that involves a small number of efficient steps to recover an effective oil emulsifier, produces no waste, and is cost-effective. The AQ product from Backtae (yellow soybean) produced emulsions with better properties (90%) than AQ produced from other cultivars and produced more stable food oil emulsions. This study will potentially lead to gluten-free, vegan products for vegetarians and consumers with animal protein allergies. This is the first report of the efficient production of AQ, an egg white substitute derived from cooked soybean of known cultivars.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.023 | 0.001 |
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; both teacher heads agree on what is shown here.
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".