Sensory Properties and Consumer Liking of Buffalo Stracchino Cheese
Bibliographic record
Abstract
The present study aims to characterize buffalo Stracchino cheese (BS) from a sensory point of view and verify how much consumers like it compared with the standard Stracchino cheese obtained from cow milk (CS). Nine panelists specifically trained to evaluate Stracchino cheese were used to conduct a quantitative descriptive sensory analysis, whereas 80 untrained consumers balanced for gender participated in the hedonic consumer test. Stracchino appearance was affected by milk type with higher intensities perceived for BS in terms of whiteness (P<0.0001) and shininess (P<0.001). As to taste and texture, BS showed higher sourness and oiliness intensities than CS, respectively (P<0.0001). Milk type did not affect the overall liking or the liking in terms of taste/flavor, texture, and appearance, but consumers rated both products at scores well above the neutral point. In addition, the liking expressed in blind conditions (i.e., without information on the milk type) was significantly lower as compared with the liking elicited by the expectations (i.e., based only on the information on the milk type) (P<0.05 and P<0.10, for CS and BS, respectively). We conclude that the good eating quality of buffalo Stracchino cheese as assessed by the consumer panel and the lack of differences between CS and BS in terms of a consumer may anticipate a possible good positioning of this novel product in the market of fresh cheese.
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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) | 0.002 | 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".