Effect of Protein Supplementation, Fat Globule Size and Storage Time on the Rheological and Sensory Properties of Buffalo Milk Stirred Yogurt
Bibliographic record
Abstract
In the present study the physicochemical (acidity, color measurements), rheological (viscosity measurements and viscoelastic properties) and sensory (objective and hedonic) properties of stirred yogurt made from buffalo milk were evaluated. Yogurt samples with two fat globule sizes (large: 2.9 ?m; small: 0.87 ?m) were prepared with or without the addition of Whey Protein Concentrates (WPC) or Sodium Caseinates (SCN), and stored at 4 °C for testing at day 1 and 7 after preparation. SCN addition resulted in increased lactic acid concentration, rheological properties (except loss tangent - tan ?), white color intensity and sensory viscosity. The effect of WPC on the aforesaid properties was lower when compared to SCN. Small fat globule size also increased the rheological properties apart from tan ?, brightness and sensory viscosity of yogurt samples. Increasing storage time resulted in reduced viscosity, increased acidity and rheological properties apart from tan ? and apparent viscosity. The greater decline rates were obvious for Newtonian viscosity at zero shear rate (?0) and instantaneous elasticity (Gg) in yogurts with added SCN and WPC and large globule size and lower or negligible in samples without additives. In contrast, tan ? increased at a lower rate when SCN were added in products with large fat globules. Storage time increased the value of flow behavior index (n) in yogurts with large fat globules and, more rapidly, the value of elastic modulus (G') in the presence of SCN and WPC. The small globule size in the absence of additives produced the lowest response of b and, when WPC was present, the lowest white color intensity. The sensory viscosity reduced the most during storage in yogurt samples with no protein addition.
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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) | 0.001 | 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".