Inactivation of <i>Salmonella</i> Enteritidis on cherry tomatoes by ultrasound, lactic acid, detergent, and silver nanoparticles
Bibliographic record
Abstract
Ultrasound (US) combined with chemical agents could represent an effective method for decontaminating fruits and vegetables. This study aimed to evaluate the use of US (40 kHz for 5 min) alone or with 1% lactic acid (LA), 1% commercial detergent (DET), or 6 mg/L silver nanoparticles (AgNP, average diameter 100 nm) as an alternative treatment to 200 mg/L sodium dichloroisocyanurate for inactivating Salmonella enterica serovar Enteritidis present on cherry tomatoes. The interfacial tension between sanitizing solutions and bacterial adhesion was investigated. Sanitizers in solutions with DET and AgNP had lower surface tension. All treatments, except that with DET, reduced Salmonella Enteritidis by more than one logarithmic cycle. There was no significant difference between the mean values of log colony-forming units (CFU)/g reduction in all treatments. Transmission electron microscopy revealed the loss of the Salmonella Enteritidis capsule following treatment with US and with US + LA. Salmonella Enteritidis counts (2.29 log CFU/g) in cherry tomatoes were markedly reduced to safe levels by treatment with the combination of AgNP and US + LA (2.37 log CFU/g).
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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) | 0.001 | 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".