Production of fresh Cheddar cheese curds with controlled postacidification and enhanced flavor
Bibliographic record
Abstract
Cheddar cheese in curd form is very popular in eastern Canada. It is retailed immediately after cheese manufacturing and can be maintained at room temperature for 24 h to provide better texture and mouthfeel. Subsequently, the cheese curds must be stored at 4 degrees C. The shelf life is generally 3 d. In this study, Cheddar cheese curds were produced by adding a high diacetyl flavor-producing strain (Lactococcus diacetylactis) to a thermophilic-based starter. The objective was to achieve both postacidification stability to increase the shelf life and enhanced flavor. The addition of L. diacetylactis increased processing time but did not affect cheese composition or the evolution of proteolysis and texture. During cheese manufacturing, streptococci became the dominant microflora in all cheeses, whereas populations of Lactococcus cremoris and L. diacetylactis decreased. During cheese storage, viable counts of L. diacetylactis and Streptococcus thermophilus increased but the counts of L. cremoris decreased. During cheese manufacturing and storage, the concentrations of lactic acid and diacetyl increased rapidly in cheeses produced with L. diacetylactis. Citric acid and galactose contents remained high in cheese made without L. diacetylactis. Sensory evaluation indicated that cheeses containing the L. diacetylactis strain were more flavorful and also had less sourness and could be stored at 4 degrees C for up to 7 d.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".