Inactivation of <i>Escherichia coli</i> O157:H7 and <i>Salmonella enteritidis</i> in Liquid Egg White Using Pulsed Electric Field
Bibliographic record
Abstract
ABSTRACT: The effects of temperature and pulsed electric field (PEF) intensity on inactivation of pathogens such as Escherichia coli O157:H7 and Salmonella enteritidis in egg white was investigated. Liquid egg white inoculated with 10 8 colony‐forming units (CFU)/mL of each pathogen was treated with up to 60 pulses (each of 2 JAS width) at electric field intensities of 20 and 30 kV/cm. The processing temperatures were 10°C, 20°C, and 30°C. After treatment, uninjured and total viable cells were enumerated in selective and nonselective agars, respectively. Maximum inactivations of 3.7 and 2.9 log units were obtained for S. enteritidis and E. coli O157:H7, respectively, while injured cells accounted for 0.5 and 0.9 logs for E. coli O157:H7 and S. enteritidis , respectively. For both bacteria, increasing treatment temperature tended to increase the inactivation rate. There was synergy between electric field intensity and processing temperature. The inactivation rate constant k T values for E. coli O157:H7 on both selective and nonselective agars were 8.2 × 10 ‐3 and 6.6 × 10 ‐3 /μS, whereas the values for S. enteritidis were 16.2 × 10 ‐3 and 12.6 × 10 ‐3 /μS, respectively. The results suggest that E. coli O157:H7 was more resistant to heat‐PEF treatment compared with S. enteritidis.
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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".