Diversity of food-borne <i>Bacillus</i> volatile compounds and influence on fungal growth
Bibliographic record
Abstract
AIMS: To evaluate the antifungal activity of the volatile organic compounds (VOCs) produced by 75 different food-borne Bacillus species against Aspergillus niger, Aspergillus flavus, Aspergillus parasiticus, Aspergillus clavatus, Fusarium oxysporum f. sp. lactucae and Moniliophthora perniciosa and to determine the VOCs responsible for the inhibition. METHODS AND RESULTS: Bacillus strains inhibited fungal growth, although with different inhibition grades, with Bacillus subtilis, Bacillus amyloliquefaciens and Bacillus cereus strains as the best antifungal VOCs producers. While M. perniciosa DM4B and F. oxysporum f.sp. lactucae MA28 were the most sensitive fungi, A. parasiticus MG51 showed the greatest resistance to Bacillus VOCs exposure. Thirty-seven compounds were detected by SPME-GC-MS analysis, although similar patterns in volatile compounds were evidenced within the species, interspecific VOCs differences determined different effects on fungal growth. Multiple partial least regression (MPLRS) and antifungal activity of the individual VOCs revealed that only propanone, 1-butanol, 3-methyl-1-butanol, acetic acid, 2-methylpropanoic acid, carbon disulphide, 3-methylbutanoic acid and ethyl acetate were responsible for mycelia inhibition of M. perniciosa DM4B and F. oxysporum f.sp. lactucae MA28. CONCLUSIONS: The antagonistic activity of the Bacillus VOCs was demonstrated, although it cannot easily be explained through the action of a single molecule, thus a holistic approach could be more appropriate to estimate the fungal growth inhibition. SIGNIFICANCE AND IMPACT OF THE STUDY: VOCs produced by Bacillus from cooked food can be considered as promising antifungal compounds useful in the control of fungal plant pathogens. This study investigates for the first time the correlation between mycelia inhibition of M. perniciosa and F. oxysporum f. sp. lactucae and the VOCs emitted by the Bacillus species.
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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 | 0.001 | 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".