Antimicrobial capacity of ultrasound and ozone for enhancing bacterial safety on inoculated shredded green cabbage (<i>Brassica oleracea</i> var. <i>capitata</i>)
Bibliographic record
Abstract
The high frequency and incidence of foodborne outbreaks related to fresh vegetables consumption is a major public health concern and an economic burden worldwide. This study evaluated the effect of individual and combined application of ultrasound (40 kHz, 100 W) and ozone on the inactivation of foodborne Escherichia coli and Salmonella, as well as their impact on cabbage color and vitamin C content. Plate count, scanning electron microscopy (SEM), and flow cytometry (FCM) following single or double staining with carboxyfluorescein diacetate and (or) propidium iodide were used to determine bacterial inactivation parameters, such as cell culturability, membrane integrity, intracellular enzyme activity, and injured and dead cells. The results of FCM and SEM showed that ultrasound treatment affected bacteria mainly by acting on the cell membrane and inactivating intracellular esterase, which resulted in bacterial death. Furthermore, when combined with ozone at 1.5 mg/L, the maximum reduction of bacterial populations was observed at 8 min with no damage on the surface of treated leaves. Therefore, fresh products sanitization using a combination of ultrasound and ozone has the potential to be an alternative for maintaining the color and vitamin C content of green cabbage.
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 | 0.000 | 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".