Salmonella Heidelberg Strain Responses to Essential Oil Components
Bibliographic record
Abstract
Salmonella are one of the more prominent foodborne pathogens that represent a major health risk to humans. Salmonella serovar Heidelberg strains are increasingly becoming an important public health concern, since they have been identified as one of the primary Salmonella serovars responsible for human outbreaks. Over the years, Salmonella Heidelberg isolates have exhibited higher rates of resistance to multiple antimicrobial agents compared to other Salmonella serovars. Essential oils (EOs) have been widely used as alternatives to chemical-based antimicrobials. In the current research, five EOs were screened to determine their antimicrobial activity against 15 S. Heidelberg strains from different sources. Oils tested were R(+)-limonene, orange terpenes, cold compressed orange oil, trans-cinnamaldehyde and carvacrol. EOs were stabilized in nutrient broth by adding 0.15% (w/v) agar. Tube dilution assays and minimal inhibitory concentrations (MIC) were determined by observing color changes in samples during exposure to EOs. Carvacrol and trans-cinnamaldehyde completely inhibited the growth of S. Heidelberg strains, while R(+)-limonene and orange terpenes did not show any inhibitory activity against the strains tested. Cold compressed orange oil only inhibited growth of two of the strains exhibiting an MIC of 1%. All S. Heidelberg isolates evaluated exhibited similar responses to the respective EOs. The use of all natural antimicrobials such as specific EOs offers the potential to limit the majority of S. Heidelberg isolates that may occur in food production.
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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) | 0.002 | 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".