Study of the antibacterial activity of electro-activated solutions of salts of weak organic acids on <i>Salmonella enterica</i>, <i>Staphylococcus aureus</i> and <i>Listeria monocytogenes</i>
Bibliographic record
Abstract
This work assessed the antibacterial activity of electro-activated solutions of salts of weak organic acids (potassium acetate, potassium citrate and calcium lactate) on Salmonella enterica, Staphylococcus aureus and Listeria monocytogenes. This activity was compared in terms of minimal inhibitory (bactericidal) concentration to the effect of commercial acetic, citric and lactic acid at equivalent titratable acidity. Staining live/dead BacLight method was used to consider physiological state of bacteria following the evaluation of pathogenic strains during exposure to the tested solutions. The results demonstrated strong inhibitory activity of all electro-activated solutions. After 10 min of exposure to electro-activated potassium acetate, a reduction of ≥6 log CFU/ml of all bacteria was observed. The electro-activated potassium citrate demonstrated the lowest minimal inhibitory concentration. Nevertheless, its inactivation power was significantly higher than that of conjugated citric acid. Although electro-activated calcium lactate was found less effective in comparison with its conjugated acid form, after 10 min of contact with the tested pathogens, it induced a population reduction of 2.23, 2.97 and 5.57 log CFU/ml of S. aureus, L. monocytogenes and S. enterica, respectively.
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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".