Selective Determination of Volatile Sulfur Compounds in Wine by Gas Chromatography with Sulfur Chemiluminescence Detection
Bibliographic record
Abstract
Volatile sulfur compounds can be formed at various stages during wine production and storage, and some may impart unpleasant "reduced" aromas to wine when present at sensorially significant concentrations. Quantitative data are necessary to understand factors that influence the formation of volatile sulfur compounds, but their analysis is not a trivial undertaking. A rapid and selective method for determining 10 volatile sulfur-containing aroma compounds in wine that have been linked to "off-odors" has been developed. The method utilizes static headspace injection and cool-on-column gas chromatography coupled with sulfur chemiluminescence detection (GC-SCD). Validation demonstrated that the method is accurate, precise, robust, and sensitive, with limits of quantitation around 1 microg/L or better, which is below the aroma detection thresholds for the analytes. Importantly, the method does not form artifacts, such as disulfides, during sample preparation or analysis. To study the contribution of volatile sulfur compounds, the GC-SCD method was applied to 68 commercial wines that had reductive sensory evaluations. The analytes implicated as contributors to reductive characters were hydrogen sulfide, methanethiol, and dimethyl sulfide, whereas carbon disulfide played an uncertain role.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".