Determination of amino acids and amines in mammalian decomposition fluid by direct injection liquid chromatography-electrospray ionisation-tandem mass spectrometry
Bibliographic record
Abstract
A sensitive and selective analytical method utilising liquid chromatography-electrospray ionisation-tandem mass spectrometry (LC-ESI-MS/MS) operated in multiple reaction monitoring mode was developed for the semi-quantitative determination of 19 biogenic amines and amino acids in mammalian (porcine) decomposition fluid. The effect of the matrix on analyte response was initially investigated by diluting crude samples and by injecting various volumes in the LC-MS system. It was found that the matrix had little effect on analyte signal intensities when small volumes (0.1 to 1 μL) of crude samples or 1 : 10 diluted samples were injected into the LC-ESI-MS/MS system. The standard addition method was also investigated for quantitative assessment but proved unsuccessful, possibly due to poor ionisation of the electrospray interface at high analyte concentrations. Therefore a method using external calibration was used for semi-quantitative determination of the analytes in decomposition fluid. The LC-ESI-MS/MS method enabled identification of all target compounds as being present. In addition a 14-day cyclic trend in the concentrations of two amino acids, phenylalanine and tryptophan, was tentatively established. General increasing trends with respect to time and temperature were also observed for putrescine and indole.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".