Effect of culture media and pH on the biomass production and biocontrol efficacy of a<i>Metschnikowia pulcherrima</i>strain to be used as a biofungicide for postharvest disease control
Bibliographic record
Abstract
Few strains of Metschnikowia pulcherrima (Pitt) M.W. Miller are under development for control of postharvest pathogens on fruit. A substrate was developed to optimize the biomass production of M. pulcherrima strain BIO126. Different complex nutrient sources, with or without pH control, were tested. Growth in yeast extract provided at concentrations > or =30 g*L-1 yielded the highest biomass. The addition of two carbon sources, D-mannitol and L-sorbose, at 5 g*L-1 each, significantly improved yeast growth. The greatest amount of yeast growth occurred when pH values of the medium ranged from 5.0 to 7.5. A combination of yeast extract, D-mannitol, and L-sorbose (YEMS), probably with diauxic utilization, showed a synergistic effect, widening the exponential phase (maximum specific growth rate of 0.45 h-1) and increasing the final cell number (1.5 x 109 cells*mL-1) and dry biomass (6.0 g*L-1) in well-controlled batch fermentation. In efficacy trials on 'Golden Delicious' apples, M. pulcherrima grown in YEMS effectively reduced incidence and severity of Botrytis cinerea (51.1% and 70.8%, respectively) and Penicillium expansum (41.7% and 14.0%, respectively). Also on 'Gala' apples, the best reduction of grey and blue mould incidence was obtained with cells grown in YEMS (58.1% and 50.5%, respectively).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".