Assessing the Impact of Smoke Exposure in Grapes: Development and Validation of a HPLC-MS/MS Method for the Quantitative Analysis of Smoke-Derived Phenolic Glycosides in Grapes and Wine
Bibliographic record
Abstract
Bushfires occur frequently in the vicinity of grape growing regions, resulting in smoke drifting over the vineyards. Wine made from smoked grapes is often downgraded or unfit for sale due to negative sensory characters. To manage or avoid the risk of producing smoke-affected wine, a diagnostic assay was developed for assessing the extent of smoke exposure in grapes and the resulting wines. The method relies on the quantitation of the glycosidic grape metabolites that are formed from major volatile phenols present in smoke. Using HPLC-MS/MS with APCI, a quantitation method for phenolic glycosides as smoke marker compounds was developed and validated. The method was confirmed to be of sufficient sensitivity and reliability to use as a diagnostic assay. On the basis of phenolic glycoside concentrations, grapes or wine can be assessed as smoke exposed or not, and the relative intensity of smoke exposure can be determined.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".