Tracing major metabolites of quinoxaline‐1,4‐dioxides in abalone with high‐performance liquid chromatography tandem positive‐mode electrospray ionization mass spectrometry
Bibliographic record
Abstract
Abstract Background Owing to the comprehensive application of quinoxaline‐1,4‐dioxides (QdNOs) in aquaculture, QdNOs and metabolites are often detected in marine food, including abalone. QdNOs are reported to exhibit cytotoxicity, photoallergy, mutagenicity, and carcinogenicity activities. To monitor for contamination of QdNOS in abalone and assess dietary exposure, a simple and reliable analytical method for the detection of QdNOs and their major metabolites was developed. Results This work is the first to present a simple and fast pretreatment procedure coupled with high‐performance liquid chromatography tandem positive‐mode electrospray ionization mass spectrometry (LC–MS/MS) for tracing of major metabolites of QdNOs in abalone. Extraction steps were simplified by the use of methanol and ethyl acetate containing 0.1% formic acid instead of more complicated acidolysis and enzymolysis pretreatment procedures. High‐sensitive characters were obtained with limits of detection ranged from 0.16 to 2.1 μg kg−1 for QdNOs and their major metabolites. Conclusion These results indicate that the LC–MS/MS method developed could be applied for QdNOs and major metabolites detection in actual samples. Considering the large production and consumption of abalone in Shandong Province, China, this work will also contribute to the further understanding of the often‐ignored exposure pathway of QdNOs. © 2019 Society of Chemical Industry
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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 | 0.001 | 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) | 0.001 | 0.001 |
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".