Chitosan–ZnO nanocomposite coating for inhibition of <i>Listeria monocytogenes</i> on the surface and within white brined cheese
Bibliographic record
Abstract
Several types of cheeses including white brined cheese have been linked to listeriosis outbreaks worldwide. This study aimed to (i) investigate the in vitro inhibitory activity of zinc oxide (ZnO) nanoparticles (NPs) at concentrations of 0.0125-0.1% against three Listeria monocytogenes strains at 10 or 37°C, (ii) evaluate the antimicrobial efficiency of chitosan-based coating containing 1.0% ZnO NPs against L. monocytogenes on the surface or inside vacuum-packaged white brined cheese at 4 or 10°C, and iii) determine the migration of ZnO NPs from the surface to the interior of cheeses using energy dispersive X-ray analysis (EDX). The antimicrobial activity of ZnO NPs was higher at 37°C than at 10°C. The initial numbers (4.0 log CFU/ml) of two L. monocytogenes strains were reduced below detectable levels, while the third strain was reduced by 1.2 log CFU/ml at 37°C. At 10°C, the initial L. monocytogenes numbers were reduced by 0.4-1.9 log CFU/ml. Chitosan coating containing 1.0% ZnO NPs reduced L. monocytogenes numbers by 1.5 and 3.7 log CFU/g on the surface or by 0.9 and 1.5 log CFU/g in the interior of vacuum-packed cheese stored at 10 or 4°C, respectively. The EDX results showed that ZnO NP levels remained constant on the cheese surface with no indication of migration into the cheese matrix at the end of storage. Practical Application: Chitosan and ZnO are antimicrobial agents and their combination in edible coatings has the potential for inactivating foodborne pathogens. Chitosan coating containing ZnO NPs can be used as an effective active packaging material to reduce numbers of L. monocytogenes in white brined cheese.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".