Impact of liposome-encapsulated enzyme cocktails on cheddar cheese ripening
Bibliographic record
Abstract
Abstract Cheddar cheese proteolysis and lipolysis were accelerated using liposome-encapsulated enzymatic cocktails. Flavourzyme, neutral bacterial protease, acid fungal protease and lipase (Palatase M) were individually entrapped in liposomes and added to cheese milk prior to renneting. Flavourzyme was tested alone at three concentrations (Z1, Z2 and Z3 cheeses). Enzyme cocktails consisted of lipase and bacterial protease (BP cheeses), lipase and fungal protease (FP cheeses) or lipase and Flavourzyme (ZP cheeses). The resulting cheeses were chemically, rheologically and organoleptically evaluated during 3 months of ripening at 8 °C. Levels of free fatty acids and appearance of bitter and astringent peptides were measured. Certain enzyme treatments (BP and ZP) resulted in cheeses with more mature texture and higher flavor intensity in a shorter time compared with control cheeses. No bitter defect was detected except in 90-day-old FP cheese. A full aged Cheddar flavor was developed in Z3 and ZP cheeses, while treatment BP led to strong typical Cheddar flavor by the second month and did not exhibit any off-flavor when ripening was extended for a further month.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".