Antimicrobial effects of chitosan and garlic against <i>Salmonella</i> spp., <i>Escherichia coli</i> O157:H7, and <i>Listeria monocytogenes</i> in hummus during storage at various temperatures
Bibliographic record
Abstract
The study aimed to evaluate the antimicrobial activity of 0.5 or 1% (w/w) chitosan and 1% (w/w) garlic against Salmonella spp., Escherichia coli O157:H7 and Listeria monocytogenes in hummus dip stored at 4, 10, or 25°C for 28, 21, or 7 days, respectively. In hummus without garlic, at all storage temperatures and storage periods, 0.5% chitosan decreased Salmonella spp., E. coli O157:H7, and L. monocytogenes by 0.9-2.3, 0.6-2.3, and 0.9-1.3 log CFU/g, respectively. In comparison, 1% chitosan decreased the numbers by 1.6-2.9, 1.4-2.7, and 1.3-1.8 log CFU/g, respectively. In hummus with 1% garlic, 0.5% chitosan decreased Salmonella spp., E. coli O157:H7, and L. monocytogenes by 0.7-2.5, 0.6-2.2, and 1.0-1.5 log CFU/g, respectively. Furthermore, 1% chitosan decreased the numbers by 1.6-2.8, 1.2-2.7, and 1.5-1.6 log CFU/g, respectively. With few exceptions, adding 1% garlic to hummus did not result in any significant reduction (at p < 0.05) in microbial numbers. The greatest decreases of Salmonella spp., E. coli O157:H7, and L. monocytogenes were 3.1, 3.6, and 2.9 log CFU/g with 1% chitosan held at 4°C for 28 days. The highest overall acceptability was for hummus with 0.5% chitosan + 1% garlic. Commercial use of chitosan is expected to help producers improve hummus safety. PRACTICAL APPLICATION: Hummus is consumed worldwide as a dip due to its taste and health benefits. Microbial safety of hummus can be enhanced by incorporating chitosan, derived from the natural polymer chitin, into the formulation. This enhanced recipe would be a bonus for producers and consumers alike.
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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) | 0.001 | 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".