Volatile aroma composition and sensory profile of Shiraz and Cabernet Sauvignon wines produced with novel<i>Metschnikowia pulcherrima</i>yeast starter cultures
Bibliographic record
Abstract
Background and Aims The use of non-Saccharomyces yeast strains as starter cultures for wine production has become increasingly popular, particularly due to their positive effect on wine composition, colour, aroma and flavour. Here, we characterise the volatile aroma composition and the sensory profile of Shiraz and Cabernet Sauvignon wines produced with novel active dry yeast preparations of Metschnikowia pulcherrima compared to that of reference strains. Methods and Results Winemaking treatments included an uninoculated fermentation, two reference Saccharomyces cerevisiae fermentations, and sequential fermentations inoculated with either M. pulcherrima AWRI1149 or M. pulcherrima AWRI3050 and S. cerevisiae. Amplicon-based internal transcribed spacer phylotyping was used to determine microbial population dynamics during fermentation. Wines were analysed for volatile composition and subjected to sensory analysis. The M. pulcherrima strains survived and dominated in both grape cultivars, and produced distinctive wine volatile profiles depending on the inoculation treatment. These differences in volatiles resulted in significant differences for several sensory attributes. Conclusions Wines made with active dry yeast preparations of M. pulcherrima AWRI1149 and M. pulcherrima AWRI3050 were characterised by increased intensity of desirable sensory attributes and by low scores for negative descriptors. Significance of the Study This work provides winemakers with additional yeast preparations that can shape sensory profile and wine style.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".