Performance assessment of a new G12/A1 antibody-based rapid ELISA using commercially available and gluten-spiked food samples
Bibliographic record
Abstract
OBJECTIVE: Food products with <20 mg/kg gluten can be labeled 'gluten-free' according to international regulations. Several antibodies-based ELISAs have been develop to track gluten traces in food products. Among them, R5 and G12 antibody-based ELISAs are the frequently used methods. However, these antibodies have certain limitations. We evaluated the accuracy of G12/A1 antibody-based 'Glutentox ELISA Rapid G12' and compared the results with the current reference method i.e., R5 antibody-based 'Ridascreen R5 ELISA'. METHODS: In the first step, the performance of Glutentox ELISA Rapid G12 kit was inspected by determination of the threshold value i.e., > or <20 mg/kg gluten in different food products. In the second step, quantification accuracy was assessed by quantification of gluten in gluten-free food products spiked with gliadin reference material. RESULTS: In total 47 food products (naturally and labeled gluten-free, and food with traces of gluten) were included. Of them, 29 products were quantified with <20 mg/kg, and 18 with a low level of gluten by both the kits. Six out of 29 gluten-free products were used for the recovery test at different spike levels. Gluten concentration and mean recovery rates of individual kits showed consistency. CONCLUSION: GlutenTox Rapid G12 ELISA could be an appropriate choice for detecting gluten in food products but needs more in-house validation and collaborative tests.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".