Repackable microfluidic molecularly imprinted solid-phase extraction coupled with mass spectrometry (μMISPE-MS) for rapid analysis of mycotoxin in agri-foods: an example of zearalenone
Bibliographic record
Abstract
Mycotoxins are detectable in 60-80% of food crops, posing significant threats to human health and food security, and causing substantial economic losses. Most mitigation approaches focus on detecting mycotoxins with standard methods based on liquid chromatography coupled with mass spectrometry (LC-MS). Typical MS methods require extensive sample preparation and clean-up due to the matrix effect, followed by time-consuming LC separation, complicating the analysis process and limiting analytical throughput. This study reports the development of a repackable microfluidic molecularly imprinted solid-phase extraction coupled with mass spectrometry (μMISPE-MS) method for rapid detection of zearalenone in agri-food samples. Silica microspheres coated with molecularly imprinted polymers were synthesized as the sorbent for analyte enrichment and sample clean-up. A cost-effective microfluidic chip was designed and fabricated as the μMISPE platform with fully automated operation, including on-line microcolumn packing and unpacking. With optimized solvent conditions and on-chip μMISPE protocol, the entire analytical process from sample to answer was completed within 15 min and achieved high recoveries (71-94%) for corn and rice samples at residue levels of 0.05-0.5 ppm (within Canadian regulatory limits of 0.2-10 ppm). This μMISPE-MS method provides a promising tool for improving mycotoxin monitoring in agri-food systems and is generalizable to other rapid analyses of targeted chemicals in complex matrices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".