Determination of Methionine and Selenomethionine in Selenium-Enriched Yeast by Species-Specific Isotope Dilution with Liquid Chromatography−Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry Detection
Bibliographic record
Abstract
Selenomethionine (SeMet) and methionine (Met), liberated by acid hydrolysis of selenium-enriched yeast, were quantified by liquid chromatography-mass spectrometry (LC/MS) using standard additions calibrations as well as isotope dilution (ID) based on species-specific (13)C-enriched spikes. LC inductively coupled plasma mass spectrometry (ICPMS) was also employed for the quantification of SeMet, and (74)Se-enriched SeMet was used for ID calibration. The results were evaluated to ascertain the feasibility of using these methods in a campaign to certify selenized yeast. Good agreement was found between the methods, which, when averaged, gave concentrations of 5482.2 +/- 101 and 3256.9 +/- 217.4 microg/g for Met and SeMet, respectively. This corresponds to a 1.68:1 Met-to-SeMet ratio in the yeast. Quantification by ID LC/MS and LC ICPMS yields the most precise sets of results with relative standard deviations in the range 0.5-1.3% (n = 6). A total selenium concentration of 2064.6 +/- 45.4 microg/g was obtained for this yeast material. The extraction efficiency and a mass balance budget were determined. Acid hydrolysis liberated 81.0% of the total selenium present. SeMet comprised 79.0% of the extracted selenium and 63.9% of the total selenium present in the yeast.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".