Determination of Methionine and Selenomethionine in Yeast by Species-Specific Isotope Dilution GC/MS
Bibliographic record
Abstract
A method for the simultaneous determination of methionine (Met) and selenomethionine (SeMet) in yeast using species-specific isotope dilution (ID) gas chromatography/mass spectrometry (GC/MS) is described. Samples were digested by refluxing for 16 h with 4 M methanesulfonic acid. Analytes were derivatized with methyl chloroformate and extracted into chloroform for GC/MS analysis. In addition to use of commercially available 13C-enriched Met and SeMet spikes for species specific ID analysis, a 74Se-enriched SeMet spike was also available for comparison of results. In selective ion monitoring mode, the intensities of ions at m/z 221, 222, 269, 270, and 263 were used to calculate the 221/222, 269/270, and 269/263 ion ratios for quantification of Met and SeMet. Concentrations of 5959 +/- 33 and 3404 +/- 12 microg g(-1) (one standard deviation, n = 6) with relative standard deviations of 0.55 and 0.36% for Met and SeMet, respectively, were obtained using 13C-enriched spikes. A concentration of 3417 +/- 8 microg g(-1) (one standard deviation, n = 6) was obtained using the 74Se-enriched SeMet spike. The concentration of SeMet measured in the yeast is equivalent to 66.43 +/- 0.24% of total Se and 30.31 +/- 0.11% of total Met is in the form of SeMet. Method detection limits (three times the standard deviation) of 3.4 and 1.0 microg g(-1) were estimated for Met and SeMet, respectively, based on a 0.25-g subsample of yeast with 1 mL of extract used for derivatization. A similar concentration of 5930 +/- 29 microg g(-1) (one standard deviation, n = 4) for Met and a lower concentration of 2787 +/- 49 microg g(-1) (one standard deviation, n = 4) for SeMet were obtained for this yeast sample using species-specific ID analysis based on GC/MS with 13C-enriched Met and SeMet spikes when a 2-h open microwave digestion approach using 8 M methanesulfonic acid was used.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".