Accelerated Cheddar cheese ripening with encapsulated proteinases
Bibliographic record
Abstract
Abstract This work was undertaken to study the impact of bacterial and fungal proteinases encapsulated in liposomes on proteolysis and organoleptic properties of Cheddar cheese during ripening. Bacterial and fungal proteinases were encapsulated in liposomes with trapping efficiencies of 31.9 and 33.0%, respectively. The encapsulated bacterial proteinase was incorporated into Cheddar-cheese milk in order to reach enzyme concentrations of 5 × 10–6 (B1), 1 × 10–5 (B2) and 2 × 10–5 (B3) AU g–1 cheese curd, while encapsulated fungal proteinase was added in order to reach enzyme concentrations of 1 × 10–6 (F1), 2 × 10–6 (F2) and 4 × 10–6 (F3) AU g–1 cheese curd. Experimental cheeses showed slightly higher moisture content but lower protein content when compared to control cheeses. Rheological measurements revealed that experimental cheeses were slightly less firm but more brittle as compared to control cheeses. Proteolysis and texture development of experimental cheeses were faster than control cheeses. As observed by transmission-electron microscopy, liposome-treated cheeses exhibited less compact microstructure and liposomes were essentially located at the fat–casein interface. The elution patterns obtained from size-exclusion chromatography revealed that the accumulation of bitter and astringent peptides was higher in experimental cheeses than in control cheeses depending on the type and concentration of added enzyme. After 3 months ripening, the organoleptic properties of cheeses were markedly improved and no bitter off-flavour was detected among treatments except cheeses subjected to treatments B3 and F3. The present study showed that micro-encapsulated enzymes can be successfully used to accelerate cheese ripening and to avoid drawbacks resulting from the use of free enzymes. It was also concluded that the addition of encapsulated bacterial proteinase (up to 1 × 10–5 AU g–1 cheese curd) or encapsulated fungal proteinase (up to 2 × 10–6 AU g–1 cheese curd) could be recommended to accelerate the flavour and texture development of Cheddar 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 | 0.000 |
| 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".