Chitosan‐based nanofibers as bioactive meat packaging materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it