Occurrence of ochratoxin A in sweet wines produced in Spain and other countries
Bibliographic record
Abstract
A survey for the presence of ochratoxin A (OTA) was undertaken from 2001 to 2005 in 188 samples of sweet wines produced in Spain and in 102 samples originating from other countries: France (n = 49), Austria (9), Chile (9), Portugal (9), Greece (6), Italy (5), Germany (3), Hungary (2), Slovenia (2), Switzerland (2), Canada (1), Japan (1), New Zealand (1), Ukraine (1), South Africa (1) and the USA (1). The analytical method was based on immunoaffinity chromatography clean-up and high-performance liquid chromatography (HPLC) with fluorescence detection. The limit of detection (defined as a signal-noise ratio = 3) was estimated to be 0.01 microg l(-1). The limit of quantification (0.02 microg l(-1)) was checked as being the lowest measurable concentration. OTA was detected in 281 out of 290 samples analysed (96.9% positive) at concentrations ranging from 0.01 to 4.63 microg l(-1). The overall mean and median levels were estimated to be 0.50 and 0.14 microg l(-1), respectively. In Spanish sweet wines OTA was found in 99% of the samples, with mean and median values of 0.65 and 0.19 microg l(-1), respectively. The mean value obtained in this study for OTA in the Spanish sweet wines would result in an intake of about 37.5 and 3.2 ng day(-1) of OTA for regular consumers and for the overall population, respectively. These figures represent a minor contribution to the provisional tolerable weekly intake (PTWI) or TWI established by the Joint Expert Committee on Food Additives (JECFA) and the European Food Safety Authority: 3.8 and 3.1% for regular consumers; and 0.4 and 0.3% for the whole adult population, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".