Identification and Quantification of a Marker Compound for ‘Pepper' Aroma and Flavor in Shiraz Grape Berries by Combination of Chemometrics and Gas Chromatography−Mass Spectrometry
Bibliographic record
Abstract
'Black pepper' aroma and flavor is important to some Australian Shiraz red wine styles but the aroma compounds involved have yet to be identified, and no objective analytical method to assess 'pepper' grape aromas is available to date. Samples of potentially 'spicy'/'peppery' grapes were obtained from vineyards in South Australia and Victoria over two vintages. The important sensory attributes of the grapes, including the aroma descriptor 'pepper', were rated by a sensory panel. The sensory study revealed a strong correlation between the intensity of 'pepper' aroma and the intensity of 'pepper' flavor perceived on the palate. The grape homogenates were analyzed by static headspace GC-MS using a cool inlet system. Vectors obtained by analysis of over 13 000 individual mass spectra per grape sample were then subjected to multivariate analyses. Both principal component analysis and partial least-squares regression were used to develop multivariate models based on mass spectra and aroma descriptors to explain the intensity of the rating of the 'pepper' character. Corresponding differences in mass spectra and aroma were observed among vineyards and from the same vineyards in different years. Additional optimization of the methodology enabled selection of a single region of the GC-MS chromatogram that allowed prediction of 'pepper' aroma intensity with a correlation coefficient >0.98 and led to the identification of alpha-ylangene, a tricyclic sesquiterpene. To assess the potential of alpha-ylangene as a marker for this sensory characteristic, a method for alpha-ylangene analysis of grapes and wine using HS-SPME-GC-MS was developed. Although not a significant aroma compound by itself, alpha-ylangene was a satisfactory marker for the 'pepper' aroma in grapes and wine, and its concentration showed similar discrimination between 'peppery' vineyards and vintages as that obtained using the multivariate models. Despite its presence in grapes, we could not detect alpha-ylangene in wine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".