Laser ablation electrospray ionization high‐resolution mass spectrometry for regulatory screening of domoic acid in shellfish
Bibliographic record
Abstract
Rationale Domoic acid (DA) is a potent neurotoxin that accumulates in shellfish. Routine testing involves homogenization, extraction and chromatographic analysis, with a run time of up to 30 min. Improving throughput using ambient ionization for direct analysis of DA in tissue would result in significant time savings for regulatory testing labs. Methods We assess the suitability of laser ablation electrospray ionization high‐resolution mass spectrometry (LAESI‐HRMS) for high‐throughput screening or quantitation of DA in a variety of shellfish matrices. The method was first optimized for use with HRMS detection. Challenges such as tissue sub‐sampling, isobaric interferences and method calibration were considered and practical solutions developed. Samples included 189 real shellfish samples previously analyzed by regulatory labs as well as mussel matrix certified reference materials. Results Domoic acid was selectively analyzed directly from shellfish tissue homogenates with a run time of 12 s. The limits of detection were between 0.24 and 1.6 mg DA kg −1 tissue, similar to those of LC/UV methods. The precision was between 27 and 44% relative standard deviation (RSD), making the technique more suited to screening than direct quantitation. LAESI‐MS showed good agreement with LC/UV and LC/MS and was capable of identifying samples above and below 5 mg DA kg −1 wet shellfish tissue, one quarter of the regulatory limit. Conclusions These findings demonstrate the suitability of LAESI‐MS for routine, high‐throughput screening of DA. This approach could result in significant time savings for regulatory labs carrying out shellfish safety testing on thousands of samples annually. © 2016 The Authors. Rapid Communications in Mass Spectrometry Published by John Wiley & Sons Ltd.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".