Evaluation of the antimicrobial activity of grape extract against Bacillus cereus in rice
Bibliographic record
Abstract
The antimicrobial potential of grape extract was assessed in cooked rice against Bacillus cereus. Grape extract efficacy was tested at 1, 5 and 10 mL/L, at pH 4.5, 5.5 and 6.5; and at incubation temperatures simulating different storage scenarios, specifically temperature abuse (10 °C), cool chain break (20 °C) and optimal B. cereus growth temperature (30 °C). Survival curves for grape extract concentration versus time were obtained. The results indicate that antimicrobial activity of grape extract was dependent on temperature, pH and grape extract concentration. A bactericidal effect of the grape extract was shown at concentration levels ≥ 5 mL/L at all temperatures and pHs studied. Inactivation curves of B. cereus under grape extract exposure were fitted to a Weibull distribution function for 5–10 mL/L grape extract concentration. Observations showed that the higher the incubation temperature and grape extract concentration, the lower the kinetic rate value. In other words, lower resistance of the microorganism to environmental conditions. The maximum inactivation level was 6 log10 cycles after 24 h of exposure at 10 mL/L of grape extract concentration and pH 4.5. Results indicate that the grape extract could be a good additional control measure for preventing Bacillus cereus growth in cooked rice 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) | 0.000 | 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) | 0.000 | 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".