Development of an Egg Detection Sandwich ELISA Kit Using Monoclonal Antibodies
Bibliographic record
Abstract
還元カルボキシメチル化した卵のオボアルブミンを免疫抗原として作製したモノクローナル抗体を用いて,特定原材料の通知法の抽出方法に対応した,新たな卵 ELISA キットを構築した.(1)作製したモノクローナル抗体は,2-ME と SDS で可溶化されたオボアルブミンを検出可能であった.さらに,複数のモノクローナル抗体を組み合わせることで,鶏,あひるおよびうずら卵白が検出可能であった.(2)構築した卵 ELISA キットの検出限界と定量限界はいずれも食品試料中の卵タンパク質濃度換算で1.0 μg/mL未満であり,表示が必要な数 μg/g レベルの測定には十分な感度であった.(3)添加回収試験では50 %以上,150 %以下の基準を満たし,再現性試験での CV%は全て10 %以下であった.さらに,卵以外の食品原材料では交差反応が認められなかった.(4)市販食品における食品表示と構築した卵 ELISA キットでの測定結果はすべて一致し,現行法と同等の測定値を示した.以上のことから,モノクローナル抗体のみで構築した卵 ELISA キットは,特定原材料の卵の定量検査法として通知法に示された現行法と同等に有用であることが考えられた.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".