Effect of Sodium Caseinates Addition on the Rheological Properties of Kefir during Gel Formation
Bibliographic record
Abstract
The effect of Sodium Caseinates (SCN) addition on the rheological behavior of kefir during gel formation was monitored by means of a dynamic rheometer of novel design (U-tube Rheometer). Kefir samples were prepared from homogenized and pasteurized full fat (3.5% w/w) bovine milk with or without the addition of SCN at varying concentrations (1%, 2% or 3% w/w). A fermented milk inoculum derived from kefir grains was inoculated into the heat-treated milk and incubated at 25°C until the pH dropped to 4.6. According to the results, the fermentation time required for the onset of gelation, as well as for the pH value to reach 4.6 increased with increasing SCN concentration. The beginning of cross-linking of proteins towards aggregates that leads to the formation of a three-dimensional protein matrix, took place at higher pH values with increasing SCN concentration. The values of elastic modulus and loss tangent that correspond to this point decreased with increasing SCN content. The increase in SCN concentration caused the values of elastic modulus during gel formation to increase and those of loss tangent to decrease. The addition of caseins into the milk increased the number and the strength of the protein-protein interactions causing the elastic behavior of the samples to increase. The presence of SCN into the system of kefir greatly affected the formation of the kefir gel.
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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 | 0.001 |
| 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) | 0.001 | 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".