Microbial Decontamination of Fresh Produce (Strawberry) Using Washing Solutions
Bibliographic record
Abstract
This study was carried out to determine the effect of natural antimicrobial washing solutions against microbial growths on fresh produce specifically strawberries. Selected washing solutions used for strawberry washing, and treatments were sterile water (control), white vinegar (VI), crude lemon juice extract (LE), VI+Origanum oil (VIO), LE+Origanum oil (LEO), and VI+LE+Origanum oil (VILEO). From the preliminary study of antimicrobial activity of washing solutions in aqueous model system tested at 2, 5, 10, 15, 20 and 25 min against S. Typhimurium, washing time was determined as 5 min to be used for this study. After the washing, strawberries were stored at 4 °C for 5 days. Results showed that all natural washing solutions exhibited inhibitory effect against total aerobic bacteria, yeast and mold. On day 5, compared to the control, all washing solutions significantly reduced S. Typhimurium by 2.7 Log CFU/g (P<0.05). Color results showed that samples color were slightly changed by washing with VIO and VILEO. However, there was no significant difference in total color change on strawberries compared to the control (P>0.05). Based on the results, it is indicated that the combination of vinegar with crude lemon juice extract and essential oil might be suitable as natural sanitizer for decontamination of fresh produce.
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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".