Optimization of the Rheological and Sensory Properties of Stirred Yogurt as Affected by Chemical Composition and Heat Treatment of Buffalo Milk
Bibliographic record
Abstract
The effects of fat content and the supplementation of milk with Sodium Caseinates (SCN) and Whey Proteins Concentrates (WPC) on the rheological and sensory properties of stirred yogurt made from buffalo milk were investigated. Whether the heat treatment of the milk affected the rheological behavior and the sensory characteristics of the samples was also evaluated. Principal Component Analysis (PCA) was used to assess in detail the relative contribution of whey proteins, caseins and fat on the rheological properties and sensory characteristics of the samples. Furthermore, it related the instrumental and objective sensory data to consumer perception (hedonic response of non-trained panelists). The objective acidity and white color intensity were positively correlated and increased with increasing casein content. Fat interacted synergistically with caseins to increase all the hedonic attributes, apart from odor. As far as rheological properties are concerned, elastic modulus (G'), instantaneous elasticity (Gg), retarded elasticity (GR) and Newtonian viscosity (?0) were positively correlated with increasing casein content. However, tan ? was negatively correlated with the aforesaid attributes and increased with increasing fat content. Whey proteins in the presence of fat determined the magnitude of flow behavior index (n). The lactic acid concentration (%) and the b component of color (yellow color intensity) were affected positively by SCN and WPC addition but in the absence of fat. In all regression equations the effect of process temperature was found to be insignificant. Finally, the consumer-optimized composition of the fat and the added SCN can be used to formulate a marketable product.
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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".