The Impacts of Frozen Material-Other-Than-Grapes (MOG) on Aroma Compounds of Cabernet Franc and Cabernet Sauvignon
Bibliographic record
Abstract
An undesirable sensory attribute (“floral taint”) has been recently detected in red wines in some winegrowing jurisdictions in North America (e.g. Ontario, British Columbia, Washington), caused by introduction of frost-killed leaves and petioles [materials-other-than-grapes (MOG)] during mechanical harvest and winemaking. It was hypothesized that terpenes, norisoprenoids, and higher alcohols would be the main responsible compounds. Objectives were to investigate causative volatile compounds for floral taint, and explore threshold concentrations for this problem. Commercial wines displaying varying intensities of floral taints were subjected to GC-MS and sensory analysis. Several odor-active compounds were higher in floral-tainted wines, including terpenes (geraniol, citronellol, cis- and trans-rose oxide), norisoprenoids (β-damascenone, β-ionone), five ethyl esters, and three alcohols. Thereafter, fermentations of Cabernet franc (CF) and Cabernet Sauvignon (CS) (2016, 2017) were conducted. MOG treatments were (wt/wt): 0, 0.5%, 1%, 2% and 5% petioles, and 0, 0.25%, 0.5%, 1%, and 2% leaf blades. Terpenes (linalool, geraniol, nerol, nerolidol, citronellol, citral, cis- and trans-rose oxides, eugenol, myrcene), norisoprenoids (α- and β-ionone), and others (e.g. hexanol, octanol, methyl and ethyl salicylate) increased linearly/quadratically with increasing MOG levels in both cultivars. Principal components analysis separated MOG treatments from the controls with 5% petioles and 2% leaves as extremes. Increasing MOG levels in CF wines increased floral aroma intensity, and were primarily associated with terpenes, higher alcohols, and salicylates. Increased leaf levels in CF were associated with higher vegetal and earthy attributes. Increased petioles in CS were not correlated with floral aromas, but increased leaves increased floral, vegetal and herbaceous attributes. Overall, petioles contributed more to floral taint than leaves through increased terpenes and salicylates (floral notes), while leaves contributed norisoprenoids and C6 alcohols (green notes).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".