Reducing variability in the measurement of gluten contamination in oats, oilseeds, and pulses by improving sample preparation
Bibliographic record
Abstract
Abstract Background and objectives The variability in gluten in non‐gluten‐containing grains (NGCG) processed using two preparation schemes was evaluated with the aim of minimizing effects of sample heterogeneity on gluten determined by enzyme linked immunosorbent assay (ELISA). The relationship between gluten concentration as determined by ELISA and visually assessed contamination of NGCG with wheat, durum, barley, and rye was investigated. Findings Low variability between duplicate aliquots taken from test portions (0–30.6% relative standard deviation [RSD]) demonstrated the ELISA itself was precise. In the first scheme, variability among test portions ranged from 1% to 143% RSD, with only half in the range of 1–50%. Using scheme 2, variability in gluten among test portions ranged from 1% to 85% RSD, with more than three quarters in the range of 1–50%. High lipid content hemp seed was a particular challenge to grind, and this was reflected in higher variability in gluten results between test portions (mean RSD = 61%). Conclusions Subsampling ground samples using rotary sample division and the use of a 1‐g test portion in scheme 2 decreased the variability of gluten results for most samples. At concentrations relevant to existing thresholds of gluten contamination (e.g. 20 mg/kg), there was no relationship between gluten concentration in NGCG and cereal contamination as determined by visual inspection. Significance and novelty This study provides guidance on how to improve the analysis of gluten contamination in NGCG by ELISA and describes the absence of a relationship between ELISA‐determined gluten and the visual assessment of contamination in NGCG.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".