Evaluation of Immunochromatographic Test Kits for Food Allergens Using Processed Food Models
Bibliographic record
Abstract
It has been mandatory to label five allergenic substances (AS; egg, milk, wheat, buckwheat and peanut) in all processed foods, since April 2002 in Japan. Two kinds of ELISA kits have been provided as screening test kits for the Japanese official method. The kits have many advantages but some disadvantages, i.e., the kits are not necessarily suitable for daily monitoring in food manufacturing plants, because they require various analytical equipments and the use of complicated procedures. To overcome these drawbacks, we have developed other diagnostic kits based on immunochromatography that should enable more rapid and simple screening for food allergens. Then we examined the performance of these immunochromatographic test kits (IC kits) in terms of sensitivity, repeatability and cross-reactivity to AS proteins in 11 kinds of food models with various heating conditions and physical properties. We also examined processed food models including AS protein of constant concentration, using the IC kits and ELISA kits, and compared the results. The IC kits detected AS proteins at 5 microg/g in the extracts from processed food models, and provided highly reproducible results. Cross-reactivity among the AS proteins was not observed. The results obtained using the IC kits showed performance equivalent to that of the ELISA kits we examined in unheating processed food models including AS proteins of constant concentration. The IC kits should be more suitable for daily monitoring in food manufacturing plants.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".