Immunochemical-Based Method for Detection of Hazelnut Proteins in Processed Foods
Bibliographic record
Abstract
A competitive enzyme-linked immunosorbent assay (ELISA) was developed to detect hazelnut by using polyclonal antibodies generated against a protein extract of roasted hazelnut. No cross-reactivity was observed in tests against 39 commodities, including many common allergens, tree nuts, and legumes. Hazelnut protein standard solutions at 0.45 ng/mL [inhibition concentration (IC80) of the competitive test] were clearly identified by the ELISA. An extraction and quantification method was developed and optimized for chocolate, cookies, breakfast cereals, and ice cream, major food commodities likely to be cross-contaminated with undeclared hazelnut during food processing. No sample cleanup was required when extracts were diluted 10-fold. Recovery results were generated with blank matrixes spiked at 4 levels from 1 to 10 microg/g hazelnut protein. With the developed extraction and sample handling procedure, hazelnut proteins were recovered at 64-83% from chocolate and at 78-97% from other matrixes. A confirmatory technique was developed with sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western transfer. The developed methods were applied to a small market survey of chocolate products and allowed the identification of undeclared hazelnut in these products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".