Chitosan‐based nanofibers as bioactive meat packaging materials
Bibliographic record
Abstract
Shelf life and safety of minimally processed food are crucial for both consumers and the food industry. This study investigates the in vitro and in situ efficiency of electrospun chitosan‐based nanofibers (CNFs) as inner part of a multilayer packaging in maintaining the quality of unprocessed red meat. Activated CNF‐based packaging (CNFP) were obtained by direct electrospinning of chitosan/poly(ethylene oxide) solutions on top of a conventional multilayer food packaging. The electrospinning solutions were firstly characterized at the molecular level, mainly in terms of zeta potential and viscoelastic properties, and the evolution of the conformational structure was correlated to the nanofiber formation process. The oxygen and water vapor barrier properties of CNF‐based (CNFP) meat packaging were also investigated. The in vitro antibacterial activity of CNFs was determined against Escherichia coli, Salmonella enterica serovar Typhimurium, Staphylococcus aureus, and Listeria innocua, bacteria commonly incriminated in the alteration of food products. The efficiency of the CNFP materials against meat spoilage by E. coli was also assessed. Our results indicate that the electrospinning of CS is a multifactorial process and fiber formation requires the choice of a good solvent, high electrical conductivity, moderate surface tension, optimum viscoelastic properties, and sufficient chain flexibility and entanglement. The results also indicate that all the tested bacterial strains were significantly sensitive to the action of CNFs. The in situ bioactivity against E. coli showed the potential of CNFP as bioactive nanomaterial barriers to meat contamination by extending the shelf life of fresh meat up to 1 week.
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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".