Gels and gelled emulsions prepared by acid-induced gelation of mixtures of faba bean (Vicia faba) protein concentrate and λ-carrageenan
Bibliographic record
Abstract
In this study, gels were successfully prepared at room temperature from mixtures of dry fractionated faba bean protein concentrate (FPC) and λ-carrageenan (λ-CGN), through acidification with glucono-δ-lactone (GDL). At neutral pH, the mixtures were shear thinning liquids, although the shear viscosity increased dramatically with λ-CGN addition. After adding GDL, the gelling kinetics were followed through small amplitude oscillatory rheology for 19 hours, at which point all gels had reached a gel modulus plateau. Elastic moduli for the prepared gels were in the range of 1500 – 4500 Pa, dependent on FPC:λ-CGN ratio and concentrations, and final pH (3.5 – 4). Rheological data further indicated the gels had properties typical of aggregated particle gels, e.g., low yield strains (∼1%). All gels showed some syneresis upon centrifugation (2000g), with the least amount of syneresis (15 – 20%) at the highest λ-CGN concentrations (1.5 – 2%). FPC is a good emulsifier, and gelled emulsions were successfully prepared. Inclusion of emulsion droplets had significant impact on the gel network, with ∼40% and ∼60% increased gel storage modulus at 20% and 30% oil, respectively. Preparing similar formulations using a more extensively processed commercial faba bean protein isolate was also attempted, but this resulted in poor gels with very high syneresis. This indicates that dry fractionation methods may be beneficial to preserve native protein functionality.
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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".