Effects of Frozen Materials Other Than Grapes on Red Wine Aroma Compounds. Impacts of Harvest Technologies
Bibliographic record
Abstract
An undesirable sensory attribute (“floral taint”) has been detected in red wines in North America, caused by leaves and petioles (materials other than grapes [MOG]) introduced during mechanical harvest after killing frosts. From 2017 to 2019, several harvest strategies were evaluated on Ontario Cabernet franc: hand harvest (HH), conventional machine harvesting (MECH), Braud-New Holland Opti (OPTI), Gregoire GL8, MECH + optical sorting (MECH+OS), and MECH with preharvest leaf removal (MECH+BLR). Concentrations of 41 odor-active compounds were quantified by gas chromatography-mass spectrometry. Harvest treatment effects varied by season. In 2017, HH resulted in lowest ethyl isobutyrate (MECH+BLR), ethyl nonoate, cis-linalool oxide (plus MECH and OPTI), trans-linalool oxide (plus MECH+OS), β-citral, and cis- and trans-rose oxide (plus MECH and OPTI). Ethyl hexanoate was lowest in MECH, and MECH+BLR, isoamyl hexanoate was lowest in all treatments except HH, and α-ionone was lowest in MECH and MECH+BLR. In 2018, HH resulted in the lowest β-damascenone, ethyl salicylate (plus OPTI and Gregoire), citronellol (plus Gregoire), cis- and trans-rose oxide (plus Gregoire), and eugenol (plus Gregoire). Isobutyl acetate, isoamyl hexanoate, and nerol were additionally reduced by Gregoire, and isopropylmethoxypyrazine was reduced by all treatments except HH. In 2019, harvest strategy affected 27 of 41 compounds, including 11 esters and 12 terpenes. Treatments leading to lowest concentrations were HH (nine compounds), MECH (eight compounds), MECH+BLR (10 compounds), OPTI (21 compounds), Gregoire (10 compounds), and MECH+OS (22 compounds). Wines from fruit that had undergone a killing frost contained different concentrations of 14 and eight compounds (2018), and 17 and 13 compounds (2019) for Cabernet franc and Cabernet Sauvignon, respectively. Results suggest that specific harvest technologies can reduce MOG and associated increases in aroma compounds, although seasonal differences may occur.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".