Relationship between Baked‐Cheese Sensory Properties and Melted‐Cheese Physical Characteristics
Bibliographic record
Abstract
Abstract Manufacturing conditions affect the sensory properties of baked Mozzarella cheese. However, instrumental analysis of cheese at room temperature does not fully correlate with the sensory properties of cheese after baking on a cooking dish. In this study, regular and stabilized pizza cheese were aged for 10, 25, 40 or 55 days before testing. A descriptive sensory analysis was performed on cheese baked on a pizza model and was related to the rheological properties of melted cheese. The sensory firmness, breakdown resistance, rubberiness and liquid release varied among cheese types and ages. Differences among cheese types and ages were also observed in small‐strain oscillatory shear moduli measured on melted cheese cooled at different temperatures. The multiple stress creep recovery test proved to provide the best correlation with baked‐cheese sensory terms. Partial dehydration of cheese before melting, to mimic the water loss during the baking process, was shown to increase correlations. Practical Applications Ingredient cheese that are baked on pizzas are required to have specific behaviors in order to meet consumer acceptance. Hence, understanding the effect of baking on cheese sensory properties and their relationship to mechanical properties should facilitate the development of new cheese functionalities. This study proposes a descriptive sensory evaluation of pizza cheese baked on a standardized pizza model. This approach increases the robustness of the quality evaluation of cheese properties. A novel method was also tested to probe the linear and nonlinear behavior of melted cheese in an effort to reduce the gap between standard rheological procedures and the sensations experienced during the rather complex action of oral processing.
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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 | 0.001 | 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".