Immuno-Enriched Microspheres - Magnetic Blade Spray-Tandem Mass Spectrometry for Domoic Acid in Mussels
Bibliographic record
Abstract
Paramagnetic microspheres can be used in planar array fluorescence immunoassays for single or multiplex screening of food contaminants. However, no confirmation of the molecular identity is obtained. Coated blade spray (CBS) is a direct ionization mass spectrometry (MS) technique, and when combined with triple quadrupole MS/MS, it allows for rapid confirmation of food contaminants. The lack of chromatography in CBS, though, compromises the specificity of the measurement for unequivocal identification of contaminants, based on the European Union (EU) regulation. Therefore, a rapid and easy-to-use immuno-magnetic blade spray (iMBS) method was developed in which immuno-enriched paramagnetic microspheres replace the coating of CBS. The iMBS-MS/MS method was fully optimized, validated in-house following the EU 2021/808 regulation, and benchmarked against a commercial lateral flow immunoassay (LFIA) for on-site screening of DA. The applicability of iMBS-MS/MS was further demonstrated by analyzing incurred mussel samples. The combination of immunorecognition and MS/MS detection in iMBS-MS/MS enhances the measurement's selectivity, which is demonstrated by the rapid differentiation between the marine toxin domoic acid (DA) and its structural analog kainic acid (KA), which cannot be achieved with the LFIA alone. Interestingly, this first-ever reported iMBS-MS/MS method is generic and can be adapted to include any other immuno-captured food contaminant, provided that monoclonal antibodies are available, thus offering a complementary confirmatory analysis approach to multiplex immunoassay screening methods. Moreover, thanks to its speed of analysis, iMBS-MS/MS can bridge the logistics gap between future large-scale on-site testings using LFIAs and classical time-consuming confirmatory MS analysis performed in official control laboratories.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".