Validation of the RIDASCREEN® Peanut for Determination of Peanut Protein in Cookies, Milk Chocolate, Ice Cream, Trail Mix, Puffed Rice Cereals, and Granola Bar: AOAC <i>Performance Tested Method</i>SM 112102
Bibliographic record
Abstract
BACKGROUND: Regulations in many countries worldwide prescribe that peanut must be listed on food labels as a cause of food allergies. Re-evaluated voluntary incidental trace allergen labelling (VITAL) values for peanut revealed the eliciting dose (ED01) value-at which 99% of all peanut-allergic individuals will not react-is 0.2 mg peanut protein. OBJECTIVE: Validation of a sandwich ELISA based on monoclonal antibodies to detect peanut proteins. METHODS: Non-processed and processed samples are extracted by an easy procedure at 60°C within 10 min. The measurement range is between 0.75 and 6 mg/kg peanut using a national institute of standards and technology (NIST) reference material as calibrator. RESULTS: The system shows no cross-reactivity against 91 different food commodities. The LOD was 0.15 mg/kg for food matrixes such as cookies, milk chocolate, ice cream, trail mix, puffed rice cereal, and granola bar. LOQ was verified at a level of 0.75 mg/kg. Recovery studies with incurred milk chocolate and ice cream revealed consistent recoveries between 67 and 85%. Mean recoveries for incurred cookies depend on the baking temperature and time and ranged from 60 to 109%. Repeatability was between 5.2 and 12.3%, whereas relative intermediate precision was between 6.4 and 13.0%. The results for incurred cookies and milk chocolate in the independent laboratory study showed mean recoveries between 99 and 104% with RSDs between 3.56 and 19.5% under repeatability conditions. CONCLUSION: The results from the in-house validation study and the independent lab confirmed that the method is accurate and in accordance with requirements laid down in Standard Method Performance Requirement 2017.020. HIGHLIGHTS: RIDASCREEN® Peanut quantifies proteins from peanut in a wide range of food categories.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".