Impact of Nisin Producing Culture and Liposome-encapsulated Nisin on Ripening of Lactobacillus added-Cheddar Cheese
Bibliographic record
Abstract
This study aimed to evaluate the effects of incorporating liposome-encapsulated nisin Z, nisin Z producing Lactococcus lactis ssp. lactis biovar. diacetylactis UL719, or Lactobacillus casei-casei L2A adjunct culture into cheese milk on textural, physicochemical and sensory attributes during ripening of Cheddar cheese. For this purpose, cheeses were made using a selected nisin tolerant cheese starter culture. Proteolysis, free fatty acid production, rheological parameters and hydrophilic/hydrophobic peptides evolution were monitored over 6 mo ripening. Sensory quality of cheeses was evaluated after 6 mo. Incorporating the nisin-producing strain into cheese starter culture increased proteolysis and lipolysis but did not significantly affect cheese rheology. Liposome-encapsulated nisin did not appear to affect cheese proteolysis, rheology and sensory characteristics. The nisinogenic strain increased the formation of both hydrophilic and hydrophobic peptides present in the cheese water extract. Sensory assessment indicated that acidic and bitter tastes were enhanced in the nisinogenic strain-containing cheese compared to control cheese. Incorporating Lb. casei and the nisinogenic culture into cheese produced a debittering effect and improved cheese flavor quality. Cheeses with added Lb. casei and liposome-encapsulated nisin Z exhibited the highest flavor intensity and were ranked first for sensory characteristics.
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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".