Optimization of ASE and SPE conditions for the HPLC‐FLD detection of piperazine in chicken tissues and pork
Bibliographic record
Abstract
Abstract Accelerated solvent extraction (ASE) and solid‐phase extraction (SPE) conditions were optimized by a high‐performance liquid chromatography‐fluorescence detector (HPLC‐FLD) method for the detection of piperazine in chicken tissues and pork. Piperazine residues were determined by precolumn derivatization with trimethylamine and dansyl chloride. Samples were extracted with 2% formic acid in acetonitrile using an ASE apparatus and purified using a Strata‐X‐C SPE column. The monosubstituted product of the reaction of piperazine with dansyl chloride was 1‐dansyl piperazine (1‐DNS‐piperazine). Chromatographic separations were performed on an Athena C18 column (250 × 4.6 mm, id: 5 μm) with gradient elution using ultrapure water and acetonitrile (5:95, V/V) as the mobile phase. The calibration curves showed good linearity over a concentration range of LOQ‐200.0 μg/kg with a coefficient of determination (R2) ≥ .9992. The recoveries and relative standard deviations (RSD values) ranged from 78.49% to 97.56% and 1.19% to 5.32%, respectively, across the limit of quantification (LOQ) and 0.5, 1, and 2.0 times the maximum residue limit (MRL; μg/kg). The limits of detection (LODs) and LOQs were 0.96 to 1.85 μg/kg and 3.20 to 5.50 μg/kg, respectively. The method was successfully applied for the validation of animal products in the laboratory.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".