Potential of Recent Ambient Ionization Techniques for Future Food Contaminant Analysis Using (Trans)Portable Mass Spectrometry
Bibliographic record
Abstract
Abstract In food analysis, a trend towards on-site testing of quality and safety parameters is emerging. So far, on-site testing has been mainly explored by miniaturized optical spectroscopy and ligand-binding assay approaches such as lateral flow immunoassays and biosensors. However, for the analysis of multiple parameters at regulatory levels, mass spectrometry (MS) is the method of choice in food testing laboratories. Thanks to recent developments in ambient ionization and upcoming miniaturization of mass analyzers, (trans)portable mass spectrometry may be added to the toolkit for on-site testing and eventually compete with multiplex immunoassays in mixture analysis. In this study, we preliminary evaluated a selection of recent ambient ionization techniques for their potential in simplified testing of selected food contaminants such as pesticides, veterinary drugs, and natural toxins, aiming for a minimum in sample preparation while maintaining acceptable sensitivity and robustness. Matrix-assisted inlet ionization (MAI), handheld desorption atmospheric pressure chemical ionization (DAPCI), transmission-mode direct analysis in real time (TM-DART), and coated blade spray (CBS) were coupled to both benchtop Orbitrap and compact quadrupole single-stage mass analyzers, while CBS was also briefly studied on a benchtop triple-quadrupole MS. From the results, it can be concluded that for solid and liquid sample transmission configurations provide the highest sensitivity while upon addition of a stationary phase, such as in CBS, even low μg/L levels in urine samples can be achieved provided the additional selectivity of tandem mass spectrometry is exploited.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".