Effects of Frozen Materials Other Than Grapes on Red Wine Volatiles. Mitigation of Floral Taints by Yeast Strains
Bibliographic record
Abstract
“Floral taint”, a unique sensory defect that has been detected in red wines in North America, is caused by frozen leaves and petioles (materials other than grapes; MOG) introduced during mechanical harvest. Responsible volatiles are likely terpenes, norisoprenoids, and esters. Objectives of this study were to investigate volatile compounds that may cause floral taint and to evaluate yeast strains that could mitigate the problem for a series of different leaf- and petiole-based MOG levels. Replicate Cabernet franc fermentations (2017 to 2019) combined MOG treatments ([wt/wt]: 0, 0.5%, and 2% leaf blades, and 0, 1%, and 5% petioles) with three yeast strains (CSM, EC1118, and FX10). Increases in leaf and petiole levels resulted in primarily linear increases in many aroma compounds, particularly terpenes. Increases in leaf addition increased concentrations of nine (2017), 12 (2018), or 17 terpenes (2019). Increased petioles led to linear increases in 13 (2017), 12 (2018), or 15 terpenes (2019). Norisoprenoids and salicylates were also responsive. Yeast effects varied with leaf and petiole levels and with season. Fermentations with 2% leaves displayed yeast-related differences in nine, four, and 10 terpenes, and four, two, and nine esters in 2017, 2018, and 2019, respectively. Fermentations with 5% petioles displayed yeast-related differences in three, six, and eight terpenes, and three, two, and five esters in 2017, 2018, and 2019, respectively. Yeasts EC1118 and FX10 produced the least terpenes at all leaf and petiole levels, while CSM produced the most. Results suggest that incorporation of frozen MOG to fermentations will result in elevated concentrations of terpenes, norisoprenoids, and salicylates associated with floral taint, and that specific yeast strains may diminish this problem.
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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".