Impact of different temperature profiles on simultaneous yeast and bacteria fermentation
Bibliographic record
Abstract
Abstract Purpose The role of fermentation temperature was studied for its impact on the evolution of malolactic fermentation performed by simultaneous inoculum of yeast and bacteria in grape must. Results were discussed considering the different fermentative kinetics and the composition of obtained wines. Methods Two strains of bacteria belonging to theO. oeniandL. plantarumspecies were inoculated 24 h after the beginning of the alcoholic fermentation in 2 grape musts having different acidic and sugar profiles. Fermentations were conducted at 3 different temperature profiles (16/22 °C in 3 days, 18/24 °C in 3 days, 22/32 °C in 5 days). Evolution of microbiota was followed by flow cytometry and plate count. Chemical analysis of grape musts and wines were performed by instrumental approaches (FT-IR, enzymatic quantification of malic acid, GC-MS). Results L. plantarumresulted more efficient in malic acid consumption in the entire set of tests. These results are unexpected because, generally,Lactobacillushas been reported to be more sensitive to an oenological environment thanO. oeni. In our experiments,O. oeniresulted inhibited by the highest fermentation temperature profile, causing incomplete malic acid degradation. Similarly,S. cerevisiaeshowed a higher sensitivity to environmental limiting factors in respect to what is generally known. Differences in the chemical composition of wines were observed in relation to the bacteria strain and the temperature profile. However, the statistical treatment of data identified temperature as the main variable able to influence the features of wines. Conclusions Simultaneous inoculum of yeast and bacteria in grape must is an alternative approach in the management of malolactic fermentation which showed some interesting features. However, it is necessary to consider that the dynamics of the microbial population are different to that observed in traditional winemaking and the environmental variables act against the microorganisms in a peculiar, and in certain cases unexpected, way.
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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".