Correlation and variability between weighing, counting and analytical methods to determine ergot (Claviceps purpurea) contamination of grain
Bibliographic record
Abstract
Ergot alkaloid mycotoxins produced by the fungusClaviceps purpurea, are contaminants of cereal crops and grasses. The objectives of this study were to determine the correlation between number of ergot sclerotia and weight compared to the total ergot alkaloid concentration, to evaluate the effect of grinding process (i.e. particle size (PS)) on ergot alkaloid analysis using high performance liquid chromatography – tandem mass spectrometry, and to determine the impact of sample volume on analytical variability. This study demonstrated that correlations exist between both ergot sclerotia count (R2=0.7242,P<0.001) and ergot sclerotia weight (R2=0.9618,P<0.001) compared to the total alkaloid concentration of 6 ergot alkaloids. However, at alkaloid ergot concentrations below 350 µg/kg grain, ergot sclerotia count (R2=0.0002,P=0.956) and ergot sclerotia weight (R2=0.0064,P=0.769) were not correlated to the total alkaloid concentration. A lower variability (P=0.041), defined by coefficient of variation (CV), was observed using a commercial UDY cyclone sample mill (PS=192 µm, CV=9 µg/kg) as compared to a household coffee grinder (PS=516 µm, CV=66 µg/kg). Total amount and concentration of individual ergot alkaloids varied (P<0.05) among sclerotia of similar weight. For the analytical method, CV was numerically reduced as sample volume increased (97% CV for 75 ml to 64% CV for 1000 ml; mean of all concentrations) but increased as sample concentration declined (17% CV for 81,678 µg/kg to 284% for 35 µg/kg; mean of all sample volumes). This implies that analysis of small sample volumes at low ergot alkaloid concentrations may result in highly variable and potentially misleading results. In conclusion, number of ergot sclerotia and weight are unreliable indicators of alkaloid content at ergot concentrations below 350 µg/kg and particle size influences the variability. An analytical approach with fine grinding (mean PS<200 µm, 85% particles <400 µm) of a large sample should be used to assess low-level ergot contamination.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".