Determination of Gliadin as a Measure of Gluten in Food by R5 Sandwich ELISA RIDASCREEN® Gliadin Matrix Extension: Collaborative Study 2012.01
Bibliographic record
Abstract
BACKGROUND: According to Codex Alimentarius, food products containing less than 20 mg/kg gluten can be labeled as "gluten-free." Since 2002, the R5 antibody method allowed determination of gluten levels and led to a huge improvement of products available to celiac disease (CD) patients. METHOD: The R5-containing test kit RIDASCREEN® Gliadin in combination with the cocktail solution was endorsed as Codex Type 1 Method in 2006 based on a collaborative study with corn-based bread, rice-based dough, wheat starches, rice, and corn flour. In 2012, the method was approved as First Action Official MethodSM2012.01 with an "in foods" claim. For Final Action in 2016, the matrix claim was reduced to rice- and corn-based matrixes. OBJECTIVE: Therefore, R-Biopharm decided to start a collaborative study to demonstrate the wide applicability of Official Method 2012.01 for the quantitative analysis of gliadin in soy, starches, pseudo cereals, legumes, spices, juice, nut nougat crème, cream cheese, pesto, meat, vegetarian meat alternative, cookies, dessert, cake, fish, bread, candies, and potatoes. Materials for incurring were the MoniQA wheat flour and the PWG gliadin preparation. RESULTS: Gliadin levels ranged from 3.4 up to 27.4 mg gliadin per kg. The results of the collaborative study with 14 participating laboratories showed recoveries ranging from 80 to 130%. Relative reproducibility standard deviations for contaminated samples were between 9.8 and 27.7%. CONCLUSIONS: The collaborative study results confirmed that the method is accurate and suitable to measure gliadin in important gluten-free food matrixes. HIGHLIGHTS: The title and applicability statement of Official Method 2012.01 were changed as proposed.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".