Accurate Determination of Malachite Green and Leucomalachite Green in Fish using Isotope Dilution Liquid Chromatography/Mass Spectrometry (ID-LC/MS)
Bibliographic record
Abstract
Malachite green (MG) has been used world-widely in aquaculture as a parasiticide or fungicide. Although MG performed successfully, it has not been permitted for use in aquaculture from European Union, USA, and Canada because of its carcinogenicity and mutagenicity. We developed a sensitive and specific method to determine MG and its principal metabolite, leucomalachite green (LMG), respectively by isotope dilution liquid chromatography mass spectrometry (ID-LC/MS). To enhance the extraction recovery of MG and LMG from fish tissue, an additional step, saponification, was introduced in sample preparation process to remove fat in sample extract, which hampered the performance of SPE columns. The residue of MG and LMG in fish was analyzed using liquid chromatography mass spectrometry in the selected ion monitoring (SIM) mode by monitoring at m/z 329 and 334 for MG and $d_5$ -MG and at m/z 331 and 337 for LMG and $^{13}C_6$ -LMG, respectively. This method was validated by comparing with the value of the reference material provided by Laboratory Government Chemistry (LGC). The results agreed within the measurement uncertainty and the accuracy was much improved than the provided reference value by LGC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".