Identification and Quantitation of 3-<i>S</i>-Cysteinylglycinehexan-1-ol (Cysgly-3-MH) in Sauvignon blanc Grape Juice by HPLC-MS/MS
Bibliographic record
Abstract
Precursors to varietal wine thiols are a key area of grape and wine research. Several such precursors, in the form of odorless conjugates, have been closely studied in recent years. A new conjugate has now been identified as 3-S-cysteinylglycinehexan-1-ol (Cysgly-3-MH), being the dipeptide intermediate between cysteine and glutathione precursors of tropical thiol 3-mercaptohexan-1-ol (3-MH). Authentic Cysgly-3-MH was produced via enzymatic transformation of the glutathione conjugate and used to verify the presence of both diastereomers of Cysgly-3-MH in Sauvignon blanc juice extracts. Cysgly-3-MH was added into our HPLC-MS/MS precursor method, and the validated method was used to quantify this new analyte in a selection of Sauvignon blanc juice extracts. Cysgly-3-MH was found in the highest concentrations (10-28.5 μg/L combined diastereomer total) in extracts from berries that had been machine-harvested and transported for 800 km in 12 h. This dipeptide conjugate was much less abundant than the glutathione and cysteine conjugates in the samples studied. On the basis of the results, the new cysteinylglycine conjugate of 3-MH seemingly has a short existence as an intermediate precursor, which may explain why it has not been identified as a natural juice component until now.
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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) | 0.001 | 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) | 0.001 | 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".