Genipin Cross-Linked Nanocomposite Films for the Immobilization of Antimicrobial Agent
Bibliographic record
Abstract
Cellulose nanocrystal (CNC) reinforced chitosan based antimicrobial films were prepared by immobilizing nisin on the surface of the films. Nanocomposite films containing 18.65 μg/cm(2) of nisin reduced the count of L. monocytogenes by 6.73 log CFU/g, compared to the control meat samples (8.54 log CFU/g) during storage at 4 °C in a Ready-To-Eat (RTE) meat system. Film formulations containing 9.33 μg/cm(2) of nisin increased the lag phase of L. monocytogenes on meat by more than 21 days, whereas formulations with 18.65 μg/cm(2) completely inhibited the growth of L. monocytogenes during storage. Genipin was used to cross-link and protect the activity of nisin during storage. Nanocomposite films cross-linked with 0.05% w/v genipin exhibited the highest bioactivity (10.89 μg/cm(2)) during the storage experiment, as compared to that of the un-cross-linked films (7.23 μg/cm(2)). Genipin cross-linked films were able to reduce the growth rate of L. monocytogenes on ham samples by 21% as compared to the un-cross-linked films. Spectroscopic analysis confirmed the formation of genipin-nisin-chitosan heterocyclic cross-linked network. Genipin cross-linked films also improved the swelling, water solubility, and mechanical properties of the nanocomposite films.
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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) | 0.000 | 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".