Antibacterial mechanism of ultrasound combined with sodium hypochlorite and their application in pakchoi (<scp><i>Brassica campestris</i> L.</scp> ssp<i>. <scp>chinensis</scp></i>)
Bibliographic record
Abstract
Abstract BACKGROUND In order to prolong the storage and inhibit microorganisms of pakchoi, the antibacterial activity and mechanism of ultrasound combined with sodium hypochlorite (NaClO‐US), the efficiency of NaClO‐US in reducing Escherichia coli , Staphylococcus aureus and Pseudomonas aeruginosa as well as preserving quality of pakchoi were investigated. RESULTS Ultrasound treatment could significantly reduce the usage of NaClO solution from 800 ppm to 500 ppm. NaClO‐US decreased the counts of E. coli , S. aureus and P. aeruginosa , which disrupted the bacterial cell membrane with cytoplasmic leakage. In addition, NaClO‐US significantly increased cell membrane permeability, while cell membrane integrity decreased, the secondary structure of bacterial proteins showed several obvious changes, such as the increase of random coil content, as well as the decrease of α ‐helix content. The bacterial counts, E. coli , S. aureus and P. aeruginosa population in pakchoi treated with NaClO‐US reduced by 1.89, 1.40, 1.60, 1.72 log CFU g −1 , respectively compared to control sample after storage for 15 days. NaClO‐US resulted in minimum chlorophyll depletion, flavor and sensory deterioration. CONCLUSION NaClO‐US solution treatment inhibited microorganisms and prolonged storage of pakchoi. © 2022 Society of Chemical Industry.
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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".