Response to trisodium phosphate treatment of<i>Salmonella</i>Chester attached to fresh-cut green pepper slices
Bibliographic record
Abstract
A laboratory model using green pepper disks was developed to investigate the attachment of Salmonella Chester on plant tissue and to evaluate the effectiveness of sanitizer agents in inactivating attached bacteria on fruits. Pepper disks (14 mm in diam, and 3-4 mm in thickness) were immersed in a bacterial suspension containing 1.5 x 107 cfu x mL(-1) of S. Chester for 30 s and subsequently air-dried at room temperature for 10 min. Approximately 30% of the bacteria retained on the disk after immersion were firmly attached and could not be removed by two washes and agitation. A positive correlation was observed between the number of bacteria attached and the concentration of bacteria in the suspension. Population studies and scanning electron microscopic examinations revealed that attachment of S. Chester on pepper disks occurred mainly on the surfaces of injured (cut) tissue but rarely on the unbroken skin. When inoculated disks were treated with 3% to 12% (w/v) of trisodium phosphate (TSP) at pH 12.3 for 5 min, the population of bacteria on the disk was reduced by 10- to 100-fold. A small portion (0.7% to 7.1%) of bacteria attached to the disk were either resistant to or protected from the TSP treatment. When the pH of TSP solution was reduced from 12.3 to 4.5, the effectiveness of TSP in inactivating S. Chester on pepper disks was reduced by 26%. This study shows that surfaces of injured fruit tissue are the principal sites for bacterial attachment, and a small portion of the bacteria attached to the tissue are resistant to the sanitizer treatment. Avoiding mechanical injuries to fresh fruits during and after harvest would reduce the chance of pathogen attachment and contamination on green pepper and fruits of similar nature.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".