<sup>82</sup>Se Metabolically-Labeled Yeast as a Matrix-Matched Isotope Dilution Standard for Quantification of Selenomethionine
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Selenized yeast is commonly used as a highly bioavailable source of selenium in dietary supplements and feed additives and is used in research settings in various disciplines due to the large number of selenium-containing metabolites formed during growth. With the selenomethionine being the major form of selenium present in selenized yeasts, its accurate quantitation is essential, however, values are frequently underestimated due to the costly and time-consuming hydrolysis-based sample preparation required to release the selenoamino acid from proteins for analysis. The National Research Council Canada has developed an 82-Se-enriched selenized yeast Certified Reference Material, SEEY-1 (DOI: 10.4224/crm.2023.seey-1) intended to be used as a matrix-matched spike material for isotope dilution analysis of selenized yeasts. The total selenium and selenomethionine contents of SEEY-1 were determined to be 322.1 ± 4.8 mg/kg ( k = 2) and 635.6 ± 16.8 mg/kg ( k = 2), respectively. Here we present results on the preparation of the 82-Se-enriched yeast, the certification process, and provide an example of the use of SEEY-1 as a matrix-matched spike for the analysis of selenomethionine in a sample of selenized yeast. We demonstrate here that SEEY-1 is able to compensate for the partial digestion of yeast proteins and provide reliable analytical data on Se amino acid content in under an hour instead of the 16 hours required for conventional complete acid hydrolysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".