Validation Procedures for Quantitative Gluten ELISA Methods: AOAC Allergen Community Guidance and Best Practices
Bibliographic record
Abstract
The food allergen analytical community is endeavoring to create harmonized guidelines for the validation of food allergen ELISA methodologies to help protect food-sensitive individuals and promote consumer confidence. This document provides additional guidance to existing method validation publications for quantitative food allergen ELISA methods. The gluten-specific criterion provided in this document is divided into sections for information required by the method developer about the assay and information for the implementation of the multilaboratory validation study. Many of these recommendations and guidance are built upon the widely accepted Codex Alimentarius definitions and recommendations for gluten-free foods. The information in this document can be used as the basis of a harmonized validation protocol for any ELISA method for gluten, whether proprietary or nonproprietary, that will be submitted to AOAC andlor regulatory authorities or other bodies for status recognition. Future work is planned for the implementation of this guidance document for the validation of gluten methods and the creation of gluten reference materials.
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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 | 0.106 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.008 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.054 |
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".