Antimicrobial Effects of Nisin, Essential Oil, and γ‐Irradiation Treatments against High Load of <i>Salmonella</i> typhimurium on Mini‐carrots
Bibliographic record
Abstract
UNLABELLED: This study aimed at using essential oil (EO) alone or combined EO with nisin and γ-irradiation to control Salmonella Typhimurium during the refrigerated storage of mini-carrots. Peeled mini-carrots were inoculated with S. Typhimurium at a final concentration of approximately 7 log CFU/g. Inoculated samples were coated by 5 different coating solutions: (i) nisin solution at final concentration of 10(3) IU/mL; (ii) mountain savory EO solution at 0.35%; (iii) carvacrol solution at 0.35%; (iv) mountain savory EO at 0.35% plus nisin solution of 10(3) IU/mL; or (v) carvacrol at 0.35% plus nisin solution of 10(3) IU/mL. Coated mini-carrots were then irradiated at 0.5 or 1.0 kGy and compared to an unirradiated control sample. Samples were kept at 4 °C and microbial analyses were conducted at days 1, 3, 6, and 9. The results showed that mini-carrots coated by carvacrol plus nisin solution or mountain savory EO plus nisin solution in combination with irradiation at 1.0 kGy completely eliminated S. Typhimurium to under the detection limit during the storage. Thus, the combined treatments using carvacrol plus nisin or mountain savory EO plus nisin coating solution and irradiation at 1.0 kGy could be used as an effective method for controlling S. Typhimurium in mini-carrots. PRACTICAL APPLICATION: This study shows the effect of using γ irradiation at low dose (1 kGy) to reduce significantly the growth of Salmonella typhimurium in mini-carrots. It also shows that combination of nisin, essential oils with γ irradiation have the best antibacterial effects against S. Typhimurium during the storage of mini-carrots. The results can be used for practical application in food industry in terms of food safety.
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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".