Assay of Ochratoxin A in Wine and Beer by High-Pressure Liquid Chromatography Photodiode Array and Gas Chromatography Mass Selective Detection
Bibliographic record
Abstract
To routinely assay the concentrations of ochratoxin A (OTA) in wines and beers, two new methods were developed and evaluated. The first utilized solid-phase extraction on a C(18) cartridge to achieve a 100-fold sample concentration followed by high-performance liquid chromatography on a C(18) column with gradient elution and quantitation at 333 nm by means of a photodiode array detector. Positive confirmation can be carried out by purity and match-factor analysis as well as peak shift following esterification with BF(3). Total run time is 28 min. The limits of detection (LOD) and quantitation (LOQ) are 0.05 and 0.10 microg/L, respectively. Recovery and imprecision ranged from 83 to 94% and from 4.0 to 8.9%, respectively. With a throughput of 35 assays per working day, this method is ideal for routine OTA analysis. It was used to survey the concentrations of OTA in 942 wines (2 of which gave values between 0.1 and 0.2 microg/L) and 107 beers (2 of which gave values between 0.05 and 0.1 microg/L). OTA was detected more frequently in red than white wines, with the highest incidence in red wines from Spain and Argentina. There was no association between OTA and country of origin or beverage type among the beers analyzed. The second method utilized gas chromatography with mass selective detection monitoring eight specific ions, preceded by extraction in dichloromethane and derivatization with bis[trimethylsilyl]trifluoroacetamide. LOD and LOQ were 0.1 and 2 microg/L, respectively; recovery and imprecision were 69-75 and 9.0-11.1%, respectively. The method is not suitable for routine quantitation but is potentially useful as a confirmatory tool for samples with OTA > or =0.1 microg/L.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".