Design of a quality control scheme to assess sample preparation performance for the determination of deoxynivalenol in wheat
Bibliographic record
Abstract
Abstract While proficiency testing is a useful tool to assess and monitor the performance of an analytical method, the use of comminuted test samples precludes the assessment of sample handling and preparation. These stages of the measurement process can introduce bias and significant variance into testing results. In this work, two approaches were used to prepare test material consisting of whole grain wheat containing a known amount of deoxynivalenol (DON) to be used to assess the variance due to sample preparation. The successful approach produced wheat kernel-like material from dough made with an aqueous DON solution. The produced material was physically similar to wheat kernels, with realistic DON content (mean 633 mg/kg) and low kernel-to-kernel variation (5% relative standard deviation). Test samples of whole grain durum wheat were prepared to approximate real-world samples with 0.2% fusarium damage. Fourteen participants analysed the whole grain test samples using their own sample preparation and analytical test methods. Calculated sample preparation variance, influenced by sub-sampling and comminution of test samples, varied from 0.0043 to 1.704 mg 2 /kg 2 . Sample preparation variance was significantly lower for participants that had comminuted the entire test sample as opposed to comminuting only a portion. The test samples produced provided participants with a straightforward and controlled process to assess their sample preparation and whether it is fit for their purpose.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".