Bibliographic record
Abstract
In order to determine the levels of ochratoxin A (OTA) in cocoa and cocoa products available in Canada, a previously published analytical method, with minor modifications to the extraction and immunoaffinity clean-up and inclusion of an evaporation step, was initially used (Method I). To improve the low method recoveries (46-61%), 40% methanol was then included in the aqueous sodium bicarbonate extraction solvent (pH 7.8) (Method II). Clean-up was on an Ochratest™ immunoaffinity column and OTA was determined by liquid chromatography (LC) with fluorescence detection. Recoveries of OTA from spiked cocoa powder (0.5 and 5 ng g(-1)) were 75-84%; while recoveries from chocolate were 93-94%. The optimized method was sensitive (limit of quantification (LOQ) = 0.07-0.08 ng g(-1)), accurate (recovery = 75-94%) and precise (coefficient of variation (CV) < 5%). It is applicable to cocoa and chocolate. Analysis of 32 samples of cocoa powder (16 alkalized and 16 natural) for OTA showed an incidence of 100%, with concentrations ranging from 0.25 to 7.8 ng g(-1); in six samples the OTA level exceeded 2 ng g(-1), the previously considered European Union limit for cocoa. The frequency of detection of OTA in 28 chocolate samples (21 dark or baking chocolate and seven milk chocolate) was also 100% with concentrations ranging from 0.05 to 1.4 ng g(-1); one sample had a level higher than the previously considered European Union limit for chocolate (1 ng g(-1)).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".