Determination of Zearalenone in Cereal Grains, Animal Feed, and Feed Ingredients Using Immunoaffinity Column Chromatography and Liquid Chromatography: Interlaboratory Study
Bibliographic record
Abstract
A method using immunoaffinity column chromatography (IAC) and liquid chromatography (LC) for determination of zearalenone in cereal grains, animal feed, and feed ingredients was collaboratively studied. The test portion is extracted by shaking with acetonitrile-water (90 + 10, v/v) and sodium chloride. The extract is diluted and applied to an immunoaffinity column, the column is washed with water or phosphate-buffered saline or methanol-water (30 + 70, v/v), and zearalenone is eluted with methanol. The eluate is evaporated, the residue is dissolved in mobile phase and analyzed by reversed-phase LC with fluorescence detection. The presence of zearalenone can be confirmed using an alternate excitation wavelength or diode array detection. Twenty samples were sent to 13 collaborators (8 in Europe, 2 in the United States, one in Japan, one in Uruguay, and one in Canada). Eighteen samples of naturally contaminated corn, barley, wheat, dried distillers grains, swine feed, and dairy feed were analyzed as blind duplicates, along with blank corn and wheat samples. The analyses were done in 2 sample sets with inclusion of a spiked wheat control sample (0.1 mg/kg) in each set. Spiked samples recoveries were 89-116%, and for the 18 naturally contaminated samples, RSDr values (within-laboratory repeatability) ranged from 6.67 to 12.1%, RSDR values (among-laboratory reproducibility) ranged from 12.5 to 19.7%, and HorRat values ranged from 0.61 to 0.90.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".