Antimicrobial Synergic Effect of Chitosan with Sodium Lactate, Nisin or Potassium Sorbate against the Bacterial Flora of Fish
Bibliographic record
Abstract
The inhibitory action of sodium lactate (SL), potassium sorbate (PS), nisin and chitosan against representative bacteria of fish spoilage flora (two Pseudomonas strains, Shewanella putrefaciens and Lactobacillus plantarum) and also against Listeria innocua was assessed. Minimum inhibitory and bactericidal concentrations were determined. Antimicrobial interactions seeking for synergistic effects between binary mixtures were evaluated by isobolograms and by the fractional inhibitory concentrations (FIC). To study antimicrobials effects on a food matrix, selected mixtures showing synergistic action were tested in fish homogenates. Most antimicrobials inhibited bacterial growth. Isobolograms and FIC index showed that the combination of antimicrobials with chitosan and the mixture PS-SL exerted a synergistic action. Among them, combinations containing PS were discarded, since the levels needed may have adverse effects on the sensory characteristics of fish. Regarding to fish homogenates, chitosan in combination with SL, achieved the greater reduction of bacterial population, being useful for the preservation of minimally processed fish.
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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) | 0.001 | 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".