Inactivation of Salmonella Enteritidis in liquid egg yolk and egg white using bacteriophage cocktails
Bibliographic record
Abstract
Salmonella Enteritidis (SE) is a significant global cause of foodborne illness, often linked to egg contamination. This study evaluated the inhibitory effects of eight bacteriophages (phages) against three SE strains isolated from poultry environments. The most effective phages were selected to formulate different phage cocktails, to enhance the efficacy and prolong inhibition. Four phage cocktails were tested at a multiplicity of infection (MOI) of 100 in tryptic soy broth (TSB), and at MOIs of 100 and 1000 in liquid egg white (EW) and egg yolk (EY) with storage at 8 °C for up to 30 days (d). The effectiveness of the phage cocktails varied significantly among bacterial strains, yet all demonstrated significant reductions compared to the positive control in liquid culture (P < 0.05). Similarly, the tested SE strains in both EW and EY showed significant reductions with phage treatments (P < 0.005), although the effectiveness was influenced by the MOI and medium composition. Treating EY proved to be more challenging, with lower magnitudes of reduction and longer treatment durations required, compared to EW. Reductions ranged from 1 to greater than 4 log CFU/mL in EW and EY after 30 d, with consistently higher reductions achieved at MOI 1000. Phage titers decreased initially, but remained stable following SE inoculation in broth and liquid eggs at 8 °C, indicating that lysis from without mechanisms may have contributed to the inhibitory effect. Notably, phages exhibited stronger attachment to SE in EW, which can be attributed to be less viscous nature of EW compared to EY. This study demonstrated that phage applications in both EW and EY effectively reduced SE counts at 8 °C, with no regrowth during long-term storage. These findings contribute to the development of biocontrol methods that enhance food safety and reduce foodborne outbreaks associated with contaminated egg products.
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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".