Mechanism of Action of Electrospun Chitosan-Based Nanofibers against Meat Spoilage and Pathogenic Bacteria
Bibliographic record
Abstract
This study investigates the antibacterial mechanism of action of electrospun chitosan-based nanofibers (CNFs), against Escherichia coli, Salmonella enterica serovar Typhimurium, Staphylococcus aureus and Listeria innocua, bacteria frequently involved in food contamination and spoilage. CNFs were prepared by electrospinning of chitosan and poly(ethylene oxide) (PEO) blends. The in vitro antibacterial activity of CNFs was evaluated and the susceptibility/resistance of the selected bacteria toward CNFs was examined. Strain susceptibility was evaluated in terms of bacterial type, cell surface hydrophobicity, and charge density, as well as pathogenicity. The efficiency of CNFs on the preservation and shelf life extension of fresh red meat was also assessed. Our results demonstrate that the antibacterial action of CNFs depends on the protonation of their amino groups, regardless of bacterial type and their mechanism of action was bactericidal rather than bacteriostatic. Results also indicate that bacterial susceptibility was not Gram-dependent but strain-dependent, with non-virulent bacteria showing higher susceptibility at a reduction rate of 99.9%. The susceptibility order was: E. coli > L. innocua > S. aureus > S. Typhimurium. Finally, an extension of one week of the shelf life of fresh meat was successfully achieved. These results are promising and of great utility for the potential use of CNFs as bioactive food packaging materials in the food industry, and more specifically in meat quality preservation.
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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".