Effects of Buffalo and Cow Milk Mixtures Enriched With Sodium Caseinates on the Physicochemical, Rheological and Sensory Properties of a Stirred Yogurt Product
Bibliographic record
Abstract
The physicochemical, rheological and sensory (objective and hedonic) properties of stirred yogurt made from buffalo and cow milk mixtures enriched with Sodium Caseinates (SCN) were evaluated. Five different milk mixtures (buffalo:cow; 0:100, 25:75, 50:50, 75:25, 100:0) with or without the addition of 1% SCN were fermented so as to produce 10 different yogurt samples. According to the results, SCN addition increased the brightness (L*), the elastic behavior, the viscosity (instrumental and sensory) and the flow behavior index (n), while it reduced the yellow color intensity (b*) of yogurt samples. Addition of milk affected significantly all the instrumental variables apart from the green color intensity (a*) and so happened but sparsely with the interactive effects between milk mixture and SCN addition. Redundancy analysis was proved a successful tool to elucidate the complex physicochemical, rheological and sensory profile of the stirred yogurt samples. Loss tanget (tan ?) and b* were indicative for high cow milk concentrations and the rest of attributes fashion with high buffalo milk concentrations, apart from n which favored samples with high cow milk enrichment and SCN addition. Panelists prefered adequately a stirred yogurt rich in buffalo milk concentration (75-100%) and low in cow milk (0-25%), enriched with SCN, with texture perceived as adequate fatty and viscous.
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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".