Characterization of key odor‐active compounds in sweet Petit Manseng ( <i>Vitis vinifera</i> L.) wine by gas chromatography–olfactometry, aroma reconstitution, and omission tests
Bibliographic record
Abstract
Petit Manseng (Vitis vinifera L.) has become a popular variety in China for the production of semisweet and sweet wines. However, few studies focused on investigating the molecular odor code of its key odorants. In this study, the key odor-active compounds of Chinese sweet Petit Manseng wine were identified by gas chromatography-olfactometry (GC-O) and gas chromatography-mass spectrometry (GC-MS). Fifty-five odorous zones were sniffed and identified by application of aroma extraction dilution analysis on a distillate prepared by liquid-liquid extraction and solvent-assisted flavor evaporation. Among them, isoamyl alcohol, ethyl octanoate, isovaleric acid, (E)-β-damascenone, and phenylethanol particularly displayed with highest flavor dilution factors above 1024. The quantification of volatiles by headspace solid-phase microextraction coupled with GC-MS and GC coupled with triple quadrupole MS/MS and a calculation of odor activity values (OAVs) indicated 23 volatiles with OAVs above 1. Ethyl hexanoate showed the highest OAV with 208.8, followed by (E)-β-damascenone (189.0), 3-mercaptohexanol (60.3), isoamyl acetate (45.4), and furaneol (40.1). The aroma of the sweet wine was successfully reconstituted by combining 42 aroma compounds in a model wine solution. 3-Mercaptohexanol, (E)-β-damascenone, furaneol, γ-octalactone + γ-decalactone + γ-hexalactone, and ethyl cinnamate had important influence on the aroma of sweet Petit Manseng wine assessed by omission tests. Moreover, the discrimination of wines from three regions was successfully achieved by partial least squares discriminant analysis based on quantitative results of key odorants. 3-Mercaptohexanol was considered as the most responsible for the region discrimination and had highest concentrations in Petit Manseng wines from Jiaodong Peninsula. PRACTICAL APPLICATION: Understanding of the knowledge in key odorants of Petit Manseng wines could be useful to improve wine quality through viticultural and enological practices.
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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 | 0.001 | 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 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".