Stability and Antimicrobial Efficacy of Reuterin and Bacteriocins (Microcin J25, Nisin Z, and Pediocin PA-1) in Chitosan- and Carboxymethyl-Cellulose-Based Hydrogels
Bibliographic record
Abstract
Traditional chemical-based sanitizers pose risks to health and the environment, highlighting the need for safer natural alternatives. We developed biocompatible hydrogels from carbohydrate-based biopolymers, chitosan (1.5% and 2.5%), and carboxymethylcellulose (CMC, 3% and 5%), each incorporating one of four antimicrobials: microcin J25, nisin Z, pediocin PA-1, or reuterin. Hydrogels were prepared by dissolving the polymers in aqueous solution and incorporating antimicrobials before gelation. The formulations were characterized using viscosity measurements, antimicrobial assays, and stability testing over 28 days of storage at room temperature (23–25 °C). Chitosan hydrogels with microcin J25 maintained strong activity against Salmonella enterica ATCC 6962, while nisin Z retained activity in gel and solution forms, though with some decline during storage. Pediocin PA-1 remained active in 1.5% and 2.5% chitosan hydrogels against Listeria monocytogenes ATCC 19115, but activity was lost in 3% and 5% CMC hydrogels. Reuterin preserved activity in CMC-based hydrogels throughout storage. In solution, microcin J25 and nisin Z consistently achieved ~7-log reductions, whereas pediocin PA-1 and reuterin reached up to ~5-log reductions. In gels, efficacy decreased at lower concentrations and shorter contact times, likely due to diffusion barriers. Overall, the hydrogels remained stable during storage, and CMC- and chitosan-based matrices with selected antimicrobials show promise as alternatives to chemical sanitizers. Their application should be tailored to specific needs, with formulations requiring longer contact times best suited for surfaces that allow prolonged exposure.
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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".