Inhibitory mechanisms of 3-methyl pentanoic acid combined with 2-methyl butyric acid and 3-hepten-2-one on <i>Bacillus</i> and their application in Lanzhou lily preservation
Bibliographic record
Abstract
AIMS: Lanzhou lily (Lilium davidli var. unicolor) possesses both nutritional and medicinal value, however, its bulbs are highly susceptible to postharvest physical damage. Pathogenic bacteria readily infiltrate through these wounds, resulting in extensive decay and consequent economic losses. This study demonstrates that optimized volatile organic compound (VOC) combinations can significantly reduce effective antimicrobial concentrations and broaden the antimicrobial spectrum through synergistic effects, while exhibiting excellent preservation potential. METHODS AND RESULTS: From decayed lily bulbs, this study isolated and identified two bacterial strains, Bacillus cabrialesii SH-3 and Bacillus amyloliquefaciens SH-5, both exhibiting opportunistic pathogenicity. Fifteen binary VOC combinations were evaluated using fractional inhibitory concentration index (FICI), identifying QM (1/8 MIC 2-methylbutyric acid + 1/8 MIC 3- methylpentanoic acid, FICI = 0.25) and QH (1/2 3-methylpentanoic acid + 1/8 3-hepten-2-one, FICI = 0.625) as optimal formulations. Mechanistic studies revealed these combinations disrupt bacterial membrane integrity, induce protein/nucleic acid leakage, and trigger oxidative stress and metabolic dysfunction. CONCLUSIONS: Against SH-3, only QM showed synergistic effects, reducing required concentrations by 87.5% compared to individual minimum inhibitory concentration (MIC). For SH-5, all combinations demonstrated additive effects, with QH achieving potent inhibition at 50% and 87.5% reduced concentrations for respective components. Applied at MIC levels, both formulations significantly suppressed postharvest decay while maintaining bulb quality.
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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".