Investigation into the Formation of Guaiacol Conjugates in Berries and Leaves of Grapevine Vitis vinifera L. Cv. Cabernet Sauvignon Using Stable Isotope Tracers Combined with HPLC-MS and MS/MS Analysis
Bibliographic record
Abstract
Fermentation of grapes that had been exposed to bushfire smoke can potentially yield unpalatable, smoke-affected wine. Guaiacol and its glucoconjugate were previously found in smoke-affected grapes at an elevated concentration. To find and identify further guaiacol conjugates in smoke-affected grapes, a stable isotope feeding experiment combined with extensive HPLC-MS and MS/MS investigations was carried out. Leaves and berries of a potted grapevine were placed in contact with an aqueous mixture of d(0)- and d(3)-guaiacol for 1-2 days and collected 5 weeks later. Screening for potential guaiacol conjugates in the leaves and berries was facilitated by monitoring the unique mass spectrometric signature of an isotopic doublet separated by 3 Da. Seven different conjugates were detected in leaves and berries and were tentatively identified as mono- and diglycosides of guaiacol. Quantitative analysis demonstrated that the guaiacol conjugates were translocated between leaves and berries to a very limited extent and were also present as low-level natural compounds of untreated leaves and berries. The same guaiacol conjugates were also found at a considerably elevated concentration in leaves and berries obtained from grapevines exposed to bushfire smoke.
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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 | 0.000 |
| 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".