The effect of Saccharomyces bayanus-mediated fermentation on the chemical composition and aroma profile of Chardonnay wine
Bibliographic record
Abstract
The use of yeasts other than Saccharomyces cerevisiae to produce wines with novel aroma and flavour profiles is gaining increased attention. The present study is concerned with the sensory impact on wine of fermentation with two selected Saccharomyces bayanus strains. The S. bayanus strains AWRI 1176 and AWRI 1375 were compared to S. cerevisiae strain AWRI 838 for their ability to affect the aroma profile and chemical composition of Chardonnay wine. Wines that were made with the S. bayanus strains contained more glycerol, succinic acid, acetaldehyde and SO2 than reference wines made with the S. cerevisiae wine yeast, but less acetic acid, malic acid and ethyl acetate. There were significant differences in the aroma profile of wines made with each yeast, as quantified by formal sensory descriptive analysis. Wines made with S. cerevisiae AWRI 838 were rated highly in the attributes ‘estery’, ‘pineapple’, ‘peach’ and ‘citrus’. Wines made with S. bayanus AWRI 1176 were rated lower than AWRI 838 in each of these attributes, but higher in the attributes ‘cooked orange peel’, ‘yeasty’, ‘nutty’ and ‘aldehyde’. The aroma profile of wine made using AWRI 1375 was different from that of wine made with the other yeasts, showing intermediate scores for the ‘estery’, ‘citrus’, ‘nutty’ and ‘aldehyde’ attributes, and no significant difference from the AWRI 838 strain in the attributes ‘pineapple’ and ‘peach’. These results demonstrate that, in addition to producing wine which has a different chemical composition, S. bayanus strains can produce a different sensory profile in wine when compared to a widely used S. cerevisiae wine yeast.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".