Evaluating the effectiveness of vinegar as a sanitizer
Bibliographic record
Abstract
Objectives: Pathogens are introduced into foods, surfaces, and hands by our surrounding environment which includes soil, air, and fecal contamination. It can be due to improper handling, cleaning, washing or sanitizing. Sanitizers are applied to surfaces in order to kill all the vegetative cells of microbes. Health Canada regulates the types, uses and concentration of the sanitizers. These sanitizers are chemically formulated to kill microbes and hence there is a rising concern about toxicity associated with their use. People are moving away from regulated sanitizers to natural alternatives. This research project examined the efficacy of vinegar, one of the natural alternatives, as a sanitizer. Methods: 3M Quick Swabs were used to collect coliform samples from a plastic cutting board before and after inoculating it with coliform culture and subsequently cleaning it with vinegar. A one tail paired t-test was conducted to assess whether the coliform counts were reduced after cleaning with vinegar. Results: For all 30 samples there was a reduction in the number of coliforms when comparing before and after cleaning with vinegar. Results show that there is a significant difference in the mean numbers of coliforms before and after cleaning with vinegar; p <0.0001. Conclusion: These results indicate that undiluted vinegar when used for cleaning food contact surface significantly reduces the coliform counts but not to the safer levels for human exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".