Immunochemical Analytical Methods for the Determination of Peanut Proteins in Foods
Bibliographic record
Abstract
Peanut proteins can cause allergenic reactions that can result in respiratory and circulatory effects in the body sometimes leading to shock and death. The determination of peanut proteins in foods by analytical methods can reduce the risk of serious reactions in the highly sensitized individual by allowing for the detection of these proteins in a food at various stages of the manufacturing process. The method performance of 4 commercially available enzyme-linked immunosorbent assay (ELISA) kits was evaluated for the detection of peanut proteins in milk chocolate, ice cream, cookies, and breakfast cereals: ELISA-TEK Peanut Protein Assay, now known as "Bio-Kit" for peanut proteins, from ELISA Technologies Inc.; Veratox for Peanut Allergens from Neogen Corp.; RIDASCREEN Peanut Kit from R-Biopharm GmbH; and ProLisa from Canadian Food Technology Ltd. The 4 test kits were evaluated for accuracy (recovery) and precision using known concentrations of peanut or peanut proteins in the 4 food matrixes. Two different techniques, incurred and spiked, were used to prepare samples with 4 known concentrations of peanut protein. Defatted peanut flour was added in the incurred samples, and water-soluble peanut proteins were added in the spiked samples. The incurred levels were 0.0, 10, 20, and 100 microg whole peanut per g food; the spiked levels were 0.0, 5, 10, and 20 microg peanut protein per g food. Performance varied by test kit, protein concentration, and food matrix. The Veratox kit had the best accuracy or lowest percent difference between measured and incurred levels of 15.7% when averaged across all incurred levels and food matrixes. Recoveries associated with the Veratox kit varied from 93 to 115% for all food matrixes except cookies. Recoveries for all kits were about 50% for cookies. The analytical precision, as measured by the variance, increased with an increase in protein concentration. However, the coefficient of variation (CV) was stable across the 4 incurred protein levels and was 7.0% when averaged across the 4 food matrixes and analytical kits. The R-Biopharm test kit had the best precision or a CV of 4.2% when averaged across all incurred levels and food matrixes. Because measured protein values varied by test kit and food matrix, a method was developed to normalize or transform measured protein concentrations to an adjusted protein value that was equal to the known protein concentration. The normalization method adjusts measured protein values to equal the true protein value regardless of the type test kit or type food matrix.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".