Liquid Chromatography with Mass Spectrometry—Detection of Lipophilic Shellfish Toxins
Why this work is in the frame
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Bibliographic record
Abstract
A rapid multiple toxin method based on liquid chromatography with mass spectrometry (LC/MS) was developed for the detection of okadaic acid (OA), dinophysistoxin-1 (DTX-1), DTX-2, yessotoxin (YTX), homoYTX, 45-hydroxy-YTX, 45-hydroxyhomo-YTX, pectenotoxin-1 (PTX-1), PTX-2, azaspiracid-1 (AZA-1), AZA-2, and AZA-3. Toxins were extracted from shellfish using methanol-water (80%, v/v) and were analyzed using a C8 reversed-phase column with a 5 mM ammonium acetate-acetonitrile mobile phase under gradient conditions. The method was validated for the quantitative detection of OA, YTX, PTX-2, and AZA-1 in 4 species (mussels, Mytilus edulis; cockles, Cerastoderma edule; oysters, Crassostrea gigas; king scallop, Pecten maximus) of shellfish obtained from United Kingdom (UK) waters. Matrix interferences in the determination of the toxins in these species were investigated. The validated linear range of the method was 13-250 microg/kg for OA, PTX-2, and AZA-1 and 100-400 microg/kg for YTX. Recovery and precision ranged between 72-120 and 1-22%, respectively, over a fortification range of 40-160 microg/kg for OA, PTX-2, and AZA-1 and 100-400 microg/kg for YTX. The limit of detection, reproducibility, and repeatability of analysis showed acceptable performance characteristics. A further LC/MS method using an alkaline hydrolysis step was assessed for the detection of OA, DTX-1, and DTX-2 in their esterified forms. In combination with the LC/MS multiple toxin method, this allows detection of all toxin groups described in Commission Decision 2002/225/EC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it