Legume milk-based yogurt mimetics structured using glucono-δ-lactone
Bibliographic record
Abstract
The potential to produce protein-structured vegan yogurts with legumes was explored to offer an alternative to conventional polysaccharide-based varieties. Glucono-δ-lactone (GDL) was employed as a slow acidifying agent and was investigated for its ability to generate cold-set, yogurt-like gels using soy and lentil milks made using minimal processing steps. Soy (5.3 % protein) and lentil (6.1 % protein) milks were successfully gelled by GDL at concentrations of 0.5 % and 1 % w/w. Soy and lentil milks experienced similar acidification profiles and demonstrated good fits with double-exponential decay models. The physical properties of these legume gels were evaluated and compared to a commercial stirred dairy yogurt. Penetration tests were carried out on intact gels, then repeated after stirring. All intact soy samples demonstrated significantly stronger gel structures compared to the commercial yogurt, and most experienced greater amounts of brittleness. Results showed that the stirring of gels caused a notable decrease in firmness and brittleness in the soy gels, making them more similar to the control. Power-law modelling of viscosity curves demonstrated that all samples experienced non-Newtonian flow behavior (n < 0.29). Susceptibility to syneresis was measured by the degree of liquid loss following centrifugation. The optimization of protein type and GDL concentration to replicate the physical properties of dairy-based yogurts can enhance their consumer acceptance and provide a more customizable and controlled approach alternative to traditional fermentation methods.
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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) | 0.000 | 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 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".