Chemical and Sensory Profiles of Merlot Wines Produced by Sequential Inoculation of Metschnikowia pulcherrima or Meyerzyma guilliermondii
Bibliographic record
Abstract
Inoculation of selected non-Saccharomyces yeasts with Saccharomyces cerevisiae as means to produce Merlot wines with reduced ethanol contents was investigated. Fermentations of grape musts (25.4° Brix, pH 3.50, and 4.23 g/L titratable acidity) were conducted in stainless steel tanks inoculated with Metschnikowia pulcherrima strains P01A016 or NS-MP or Meyerozyma guilliermondii P40D002 with S. cerevisiae Syrah added after three days. After fermentation, wines with Mt. pulcherrima contained 13.8% (P01A016) or 13.9% (NS-MP) v/v ethanol, respectively, amounts which were lower than in wines with S. cerevisiae alone (14.9% v/v). Delayed inoculation of must with S. cerevisiae (day 3) or musts with My. guilliermondii contained elevated concentrations of ethyl acetate (145 and 148 mg/L, respectively), concentrations significantly higher than those with S. cerevisiae inoculated on day 0 or with either strain of Mt. pulcherrima. Descriptive sensory analysis revealed a significant effect due to panelist but not due to Mt. pulcherrima or My. guilliermondii. This research indicates the potential for commercial application of these yeasts towards the production of reduced alcohol wines but without imparting negative sensory attributes.
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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".