Emerging analytical methods to determine gluten markers in processed foods—method development in support of standard setting
Bibliographic record
Abstract
The availability of analytical methods to detect and determine levels of markers of priority allergens in foods is of the utmost importance to support standard setting initiatives, the development of compliance and enforcement activities, as well as to provide guidance to industry on implementation of quality control practices, ensuring the effectiveness of allergen-related sanitation techniques. This paper describes the development and implementation of a mass-spectrometry-based technique to determine markers for individual sources of gluten in beer products. This methodology was shown to answer the requirements of Health Canada's proposed labeling standard for individual gluten source declaration, in order to achieve its policy objectives (i.e., protection of sensitive consumers, while promoting choice). Minimal sample work-up was required and the results obtained by ELISA were further complemented using the LC-MS/MS method. This paper aims to demonstrate the feasibility of alternative techniques to ELISA-based methodologies to determine allergen and gluten markers in food.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".