Multi-laboratory validation of the xMAP—Food Allergen Detection Assay: A multiplex, antibody-based assay for the simultaneous detection of food allergens
Bibliographic record
Abstract
The increasing prevalence of individuals with multiple food allergies and the need to distinguish between foods containing homologous, cross-reactive proteins have made the use of single-analyte antibody-based methods (e.g., ELISAs) sometimes insufficient. These issues have resulted in the need to conduct multiple analyses and sometimes employ orthogonal methods like mass spectrometry or DNA-based methods for confirmatory purposes. The xMAP Food Allergen Detection Assay (xMAP FADA) was developed to solve this problem while also providing increased throughput and a modular design suitable for adapting to changes in analytical needs. The use of built-in redundancy provides the xMAP FADA with built-in confirmatory analytical capability by including complementary antibody bead sets and secondary analytical end points (e.g., ratio analysis and multi-antibody profiling). A measure of a method's utility is its performance when employed by analysts of varying expertise in multiple laboratory environments. To gauge this aspect, a multi-laboratory validation (MLV) was conducted with 11 participants of different levels of proficiency. The MLV entailed the analysis of incurred food samples in four problematic food matrices, meat sausage, orange juice, baked muffins, and dark chocolate. Except for a couple of instances, involving two confirmatory components in the analysis of baked muffins, the allergenic foods were detected by all participants at concentrations in the analytical samples comparable to ≤ 10 μg/g in the original food sample. In addition, despite high levels of inter-lab variance in the absolute intensities of the responses, the intra-laboratory reproducibility was sufficient to support analyses based on the calibration standards and direct comparison controls (DCCs) analyzed alongside the samples. In contrast, ratio analyses displayed inter-laboratory %CV (RSDR) values < 20%; presumably because the ratios are based on inherent properties of the antigenic elements. The excellent performance of the xMAP FADA when performed by analysts of varying proficiency indicates a reliability sufficient to meet analytical needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".