Evaluation of a Real Time PCR Assay and a ELISA Method for the Detection of Walnuts and Almonds Allergen Traces in Food Products
Bibliographic record
Abstract
Food allergens are a well acknowledged issue in food industry and are regulated by legislation. The presence of allergens can either origin from the raw material or due to contamination during production. Allergen information on packaging is mandatory although it cannot be accurate in the case of contamination therefore warnings are used. The purpose of the study is the development and validation of a SYBR Green Real Time Polymerase Chain Reaction method using specific primer pairs based on Jug r 1, Jug r 3, and Jug r 4 allergen-coding sequences to improve the sensitivity of Real Time Polymerase Chain Reaction techniques for detection of walnut and almond traces in commercial food products and its comparison with ELISA methodology in terms of detection ability. A total of 100 samples were collected from local markets and were analyzed by Real Time Polymerase Chain Reaction (RT-PCR) and ELISA methods. The results indicated that 16 samples (16%) were found positive in walnut traces and 18 samples (18%) were found positive in almond traces by Real Time Polymerase Chain Reaction of which Elisa identified 14 samples (14%) positive in walnut traces and 15 samples (15%) positive in almond traces. Among them, 4 samples (25%) that contained walnut traces and 6 samples (33.3%) that contained almond traces had no allergen declaration on their label. The improved accuracy of Real Time Polymerase Chain Reaction underlines the importance of this method for allergen detection and quantification in the food industry
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".