Impact of cluster thinning and harvest date on berry volatile composition and sensory profile of <i>Vitis</i> sp. Seyval blanc and Vandal-Cliche
Bibliographic record
Abstract
Northern conditions are challenging for grape and wine producers. To cope with these challenges, the use of early ripening interspecific hybrid Vitis sp., such as Seyval blanc and Vandal-Cliche, is often preferred to traditional V. vinifera. However, knowledge about cultural practices suitable for interspecific hybrid varieties are still scarce, especially regarding their impact on profiles of volatile compounds and the sensory perception of berries. In this study, the impact of cluster thinning and harvest date on grapevine physiology, must chemical composition and berry sensory attributes of Seyval blanc and Vandal-Cliche was investigated. Three crop loads (100 %, 70 % and 40 % crop) and three harvest dates corresponding to pre-maturity, maturity, and post-maturity were assessed in 2012 and 2013. Cluster thinning significantly decreased the yield of Vandal-Cliche, but not of Seyval blanc, for which it instead improved fruit set without affecting yield. The impact of cluster thinning on must chemical composition was limited for both cultivars, although some significant changes were observed, especially in the cool season of 2013. Clear variations during ripening, in terms of technological parameters and aroma characteristics, were observed depending on the cultivars and the seasons. In Seyval blanc, C6-alcohols and C13-norisoprenoids decreased during ripening, while C6-aldehydes and linalool increased. In Vandal-Cliche, berries from the first and the last sampling dates of the warm season of 2012 showed the highest levels of C13-norisoprenoids, whereas berries from the last sampling date in the cold season of 2013 showed the highest levels of terpenes, C13-norisoprenoids and ethyl esters. Despite its limited impact on fruit chemical composition, cluster thinning significantly impacted the sensory perception of Vandal-Cliche berries in both years. Cluster-thinned berries had softer skin, sweeter and less acidic pulp, and fruitier skin and pulp aroma. Skin softness allowed excellent discrimination of the maturity of both cultivars during the two study years, suggesting that this descriptor could be a suitable maturity marker for these interspecific hybrid varieties. In summary, cluster thinning proves beneficial for enhancing fruit set in Seyval Blanc in a northern climate; however, its influence on fruit quality is minimal for both cultivars.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".