Combined effects of garlic essential oil and allyl isothiocyanate against Escherichia coli O157:H7 in vitro and in pork sausage
Bibliographic record
Abstract
ABSTRACT: Escherichia coli O157:H7 is a toxigenic serotype of E. coli and has been associated with foodborne outbreaks involving meat products, vegetables and fresh produces worldwide. Salts for curing are usually employed as antimicrobials in the production of pork sausages. However, salts do not have a significant inhibitory effect on enterobacteria. Due to the growing demand for natural foods, the use of essential oils has been proposed as natural antimicrobials in food. This study aimed to evaluate the effects of garlic essential oil (GO) and allyl isothiocyanate (AITC) against E. coli O157:H7 in vitro and in pork sausage. The Minimal Inhibitory Concentration (MIC) and Minimal Bactericidal Concentration (MBC) of these oils, alone and in combination, against E. coli O157:H7 were determined. Pork sausage was inoculated with 8log CFU/g E. coli O157:H7 and different combinations of GO and AITC. A control group was performed without essential oils. Sausages were packaged and stored at 6°C for 20 days. E. coli O157:H7 population and instrumental color (L*, a*, b*, C* and hue) determinations were performed at 5-day intervals. AITC showed lower MIC and MBC than GO. When combined, AITC and GO showed a synergistic effect. Treatments T3 and T4 showed 1,01log CFU and 1,87log CFU reduction of E. coli O157:H7 compared to control. The redness and the chroma of sausages treated with AITC and GO increased during storage. Together, GO and AITC caused minor changes in taste and flavor of sausages, and were able to reduce the population of E. coli O157:H7 and to maintain the red color of sausage during storage.
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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) | 0.001 | 0.001 |
| 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".