Determination of 5-nitro-2-furaldehyde as marker residue for nitrofurazone treatment in farmed shrimps and with addressing the use of a novel internal standard
Bibliographic record
Abstract
Abstract We developed a significantly improved ultra-high performance liquid chromatography-tandem mass spectrometry method for determination of 5-nitro-2-furaldehyde (NF) as a surrogate using a novel internal standard for the detection of nitrofurazone. We used 2,4-dinitrophenylhydrazine derivatization and furfural as the internal standard. Derivatization was easily performed in HCl using ultrasonic manipulation for 5 min followed by liquid extraction using ethyl acetate. The samples were concentrated and purified using reverse phase and alumina cartridges in tandem. The derivatives were separated using a linear gradient elution on a C18 column with methanol and water as the mobile phase in negative ionization mode and multiple reaction monitoring. Under the optimized conditions, the calibration curves were linear from 0.2 to 20 μg/L with correlation coefficients >0.999. Mean recoveries were 80.8 to 104.4% with the intra- and inter-day relative standard deviations <15% at spiking levels of 0.1 to 10 μg/kg. The limits of detection and quantification were 0.05 and 0.1 μg/kg, respectively. This method is a robust tool for the identification and quantitative determination of NF in shrimp samples.
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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 | 0.000 |
| 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 | 0.001 | 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".