Influence of cane and beet sugar for second fermentation on “fruity” aromas in Auxerrois sparkling wines
Bibliographic record
Abstract
Traditional Method sparkling wine production requires a sugar addition to the base wine to initiate the second alcoholic fermentation in bottles. This study aimed to identify differences in “fruity” volatile aroma compounds (VOCs) in Traditional Method sparkling wines produced from the addition of either cane sugar or beet sugar to Auxerrois base wines. Wines underwent a second fermentation in bottles inoculated with IOC 18-2007 yeast and fermented at 15 °C. Standard chemical analysis was carried out on base wines and sparkling wines. The concentrations of fourteen “fruity” volatile aroma compounds representing five classes of compounds were analysed by Headspace-Solid-Phase Micro-Extraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS). Cane and beet sugars were analysed in de-aromatised wine and distilled water to establish the concentrations of VOCs present in the sugar products prior to addition to wine. Wines were analysed on the day of inoculation and bottling and again after the second fermentation. Beet sugar significantly (Pt < 0.05) increased the concentration of linear fatty acid-derived ethyl esters (ethyl octanoate, ethyl hexanoate, and ethyl butyrate) compared to cane sugar in sparkling wine. These results are attributed to higher concentrations of medium-chain fatty acids found in beet sugar due to the duration of sugar beet storage prior to processing. Recommended future research includes monitoring aroma compounds during ageing on lees, sensory analysis, and an investigation of a wider range of sugar products permitted for use in sparkling wine production.
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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 | 0.001 | 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".