The determination of melamine in muscle tissue by liquid chromatography/tandem mass spectrometry
Bibliographic record
Abstract
In early 2007 it was determined that the compound melamine, suspected of having been involved in the deaths of numerous pets, had been fed to hogs intended for human consumption. This report describes a method for the analysis of melamine in porcine muscle tissue using solid-phase extraction (SPE) and high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS). Melamine was extracted in 50% acetonitrile in water. Homogenates were centrifuged and supernatants were acidified and washed with methylene chloride. The aqueous extracts were cleaned up using mixed-mode C8/strong cation exchange SPE and then concentrated, fortified with a stable isotope-labeled analog of melamine, and analyzed by HPLC/MS/MS. Gradient HPLC separation was performed using an ether-linked phenyl column with ammonium acetate/acetic acid and acetonitrile as the mobile phase. Multiple reaction monitoring (MRM) mode of two precursor-product ion transitions for melamine and one for the internal standard was used. A five point calibration curve ranging from 50 to 2000 ng/mL of melamine in solvent was used to establish instrument response. The method was validated by analysis of seven replicate porcine muscle tissue samples fortified with 10 ng/g of melamine. The mean recovery for the seven replicates was 83% with 6.5% relative standard deviation and the calculated method detection limit was 1.7 ng/g.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".