Method for the Gas Chromatographic Assay with Mass Selective Detection of Trichloro Compounds in Corks and Wines Applied To Elucidate the Potential Cause of Cork Taint
Bibliographic record
Abstract
To investigate the role of trichloro compounds as a potential cause of "cork taint" in wine, an assay for trichloroanisole (TCA) and trichlorophenol (TCP) in corks and wine was developed utilizing solid phase extraction on a C(18) cartridge followed by gas chromatography with mass selective detection. Recovery and imprecision for TCA were 86-102 and 1.6-5.8%, respectively, and for TCP 82-103% and 1.7-3.9%, respectively. Limits of detection and quantitation were 0.1 and 2 ng/L, respectively, for TCA, and 0.7 and 4 ng/L, respectively for TCP. A survey of 2400 commercial wines revealed a higher incidence of cork taint in white wine than in red and in wines utilizing composite cork closures; wines from central Europe and Spain had higher overall rates of contamination and those from Canada and Italy the lowest. Significant but modest associations were found between the TCA and TCP contents of the wines and corks, but many wines exhibiting cork taint had low or undetectable concentrations of TCA. Over a 12-month period, experimentally bottled wines exhibited a slow increase in TCA and TCP content while cork closures manifested a decrease; most bottles showing cork taint contained low levels of TCA, and TCP concentrations were well below the sensory threshold. Neither compound was cytotoxic to human cell lines in culture up to final concentrations of 500 ng/mL. It was concluded that these two trichloro compounds are, at most, minor components of cork taint in commercial wines.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".