Elaboration and Characterization of Whey Protein Beads by an Emulsification/Cold Gelation Process: Application for the Protection of Retinol
Bibliographic record
Abstract
Whey protein beads were successfully produced using a new emulsification/cold gelation method. The principle of this method is based on an emulsifying step followed by a Ca(2+)-induced gelation of pre-denatured (80 degreesC/30 min) whey protein. Beads are formed by the dropwise addition of the suspension into a calcium chloride (CaCl(2)) solution. IR results show that bead formation has a pronounced effect on the secondary structure of whey protein, which leads to the formation of intermolecular hydrogen-bonded beta-sheet structures. Their preparation conditions (CaCl(2) concentrations of 10, 15, and 20% (w/w)) influence their sphericity and homogeneity: an increase in CaCl(2) favors regular-shaped beads. The physicochemical and mechanical characterizations of beads were also carried out. Their properties, such as swelling, elasticity, deformability, and resistance at fracture, change according to pH levels (1.9, 4.5, and 7.5) and preparation conditions. Indeed, protein chain networks exhibit different behavior patterns with respect to their charge. Finally, bead degradation by enzymatic hydrolysis reveals that beads are gastroresistant and form good matrixes to protect fat-soluble bioactive molecules such as retinol, that have in vivo intestinal absorption sites. The experiment demonstrated the potential of whey protein beads to protect molecules sensitive (i.e., vitamins) to oxidation.
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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 | 0.001 | 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".