Survey of breakfast and infant cereals for aflatoxins B<sub>1</sub>, B<sub>2</sub>, G<sub>1</sub>and G<sub>2</sub>
Bibliographic record
Abstract
Three hundred and forty-nine breakfast and infant cereal samples were collected at retail level across Canada from 2002 to 2005. They included rice-, soy-, barley-based and mixed-grain infant cereals, corn-, wheat-, rice-based and mixed-grain breakfast cereals, and were analysed for aflatoxins B1, B2, G1 and G2 using a modified AOAC International official method. An immunoaffinity column was used for the cleanup and purification of extracts. Determination of aflatoxins was by LC using post-column derivatization with pyridinium hydrobromide perbromide and fluorescence detection. Results indicated that 50% of both breakfast and infant cereals had detectable levels (limit of detection = 0.002 ng g-1) of aflatoxin B1, which is the most toxic of the four toxins. The levels found varied from 0.002 to 1.00 ng g-1 for aflatoxin B1, from 0.002 to 0.14 ng g-1 for aflatoxin B2, from 0.008 to 0.27 ng g-1 for aflatoxin G1, and from 0.008 to 0.048 ng g-1 for aflatoxin G2. Only 4% of the breakfast cereals and 1% of the infant cereals had aflatoxin B1 levels exceeding 0.1 ng g-1, which is the European Union maximum limit for aflatoxin B1 in baby foods and processed cereal-based foods for infants and young children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".