Magnetic Amino-Modified Multiwalled Carbon Nanotube (MWCNT) Based Magnetic Dispersive Solid-Phase Extraction (m-dSPE) for the Determination of Paralytic Shellfish Toxins in Bivalve Mollusks with Hydrophilic Interaction Liquid Chromatography–Tandem Mass Spectrometry (HILIC-MS/MS)
Bibliographic record
Abstract
A sensitive and efficient magnetic amino modified multi-walled carbon nanotubes (m-MWCNT-NH2)-based magnetic dispersive solid-phase extraction (m-dSPE) coupled with hydrophilic interaction liquid chromatography–tandem mass spectrometry (HILIC-MS/MS) strategy is reported for the simultaneous determination of 13 paralytic shellfish toxins (PSTs) in bivalve mollusks. The samples were extracted twice with 1% acetic acid and purified using optimized m-dSPE procedures with Fe3O4 coated MWCNT-NH2 composites as magnetic adsorbents. The adsorbed PSTs on the m-MWCNT-NH2 adsorbents were separated using a Ni-coated neodymium magnet and subsequently eluted with 2 mL water–acetonitrile–acetic acid (80:20:1, v/v/v) and analyzed by HILIC-MS/MS. The correlation coefficients (r) of the targeted toxins obtained in matrix-matched external standard curves were from 0.997 to 0.999. The PSTs were acquired and quantified in the multiple reaction-monitoring mode and the limits of detection and quantitation in bivalve mollusks were 1.10 to 4.51 μg/kg and 3.67 to 13.5 μg/kg, respectively. Spiked recoveries at three concentrations in negative samples were from 73.4% to 92.5% with precision less than 10.6%. The method validation was in accordance with 2002/657/EC guidelines and method application in commercial bivalve mollusk samples. The developed method was rapid, convenient and economical for the determination of the targeted toxins.
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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 | 0.000 | 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".