Development of an Encapsulation System for the Protection and Controlled Release of Antimicrobial Nisin at Meat Cooking Temperature
Bibliographic record
Abstract
Nisin is an antimicrobial peptide produced by Lactococcus lactis spp. lactis widely investigated for use in foods as a natural antimicrobial. However, its effective use in meat products is restricted notably by its reaction with meat constituents (including glutathione) in raw meat. The purpose of this study was to develop an encapsulation system that would optimize nisin activity when used in meat. To achieve this goal, an encapsulation in dipalmitoylphosphatidylcholine (DPPC) liposomes was developed. DPPC liposomes were formed in phosphate buffer with or without nisin. The encapsulation efficiency of nisin in liposomes was greater than 46 ± 2%. The median size of nisin-loaded liposomes was 495 nm, compared to 170 nm for empty liposomes. The liposomes containing nisin were stable for up to 7 days at 4°C but a zone of inhibition was observed afterwards. Stability of the liposome to heat was also tested and demonstrated that above 37°C nisin was released from the melted liposomes to form zones of inhibition. Activity of free and encapsulated nisin was tested in raw and cooked ground beef (71°C). Free nisin lost its activity in raw beef but DPPC-encapsulated nisin remained active and was released upon melting of the liposome during heat treatment.
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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) | 0.000 | 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".