Application of isotope dilution mass spectrometry: determination of ochratoxin A in the Canadian Total Diet Study
Bibliographic record
Abstract
Analytical methods are generally developed and optimized for specific commodities. Total Diet Studies, representing typical food products 'as consumed', pose an analytical challenge since every food product is different. In order to address this technical challenge, a selective and sensitive analytical method was developed suitable for the quantitation of ochratoxin A (OTA) in Canadian Total Diet Study composites. The method uses an acidified solvent extraction, an immunoaffinity column (IAC) for clean-up, liquid chromatography-tandem mass spectrometry (LC-MS/MS) for identification and quantification, and a uniformly stable isotope-labelled OTA (U-[(13)C(20)]-OTA) as an internal recovery standard. Results are corrected for this standard. The method is accurate (101% average recovery) and precise (5.5% relative standard deviation (RSD)) based on 17 duplicate analysis of various food products over 2 years. A total of 140 diet composites were analysed for OTA as part of the Canadian Total Diet Study. Samples were collected at retail level from two Canadian cities, Quebec City and Calgary, in 2008 and 2009, respectively. The results indicate that 73% (102/140) of the samples had detectable levels of OTA, with some of the highest levels of OTA contamination found in the Canadian bread supply.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".