Evaluation of different strategies for determination of selenomethionine (SeMet) in selenized yeast by asymmetrical flow field flow fractionation coupled to inductively coupled plasma mass spectrometry (AF4-ICP-MS)
Bibliographic record
Abstract
This manuscript exemplifies the prospective use of asymmetrical flow field flow fractionation (AF4) coupled to inductively coupled plasma mass spectrometry (ICP-MS) as a simple tool for chemical speciation of selenomethionine (SeMet) in selenized yeast. Several popular sample preparation methods were evaluated for their suitability to determine selenomethionine (SeMet) in selenized yeast by AF4-ICP-MS. These included water, methanesulfonic acid (MSA), formic acid (FA) and alkaline extractions. Alkaline extraction (using sodium dodecyl sulfate buffer) provided the best recovery/determination conditions for SeMet based on analysis of NRC certified reference material (CRM) SELM-1 since it minimized hydrolysis of the protein peptide bonds optimally required for the AF4 separation. The analytical performance of three different AF4 membranes (5, 10 and 500 kDa regenerated cellulose) was also evaluated. No significant difference in the recovery of SeMet was observed when using 5 and 10 kDa RC membranes, whereas the 500 kDa membrane resulted in a significant loss. The proposed method presents appropriate instrument and intra-assay precisions of 4.4-9.2% and 3.8% RSD, respectively, a detection limit of 0.49 μg L-1 SeMet as Se and good linearity with correlation coefficients (R) between 0.996 - 0.999. This is the first report of use of AF4-ICP-MS for species specific quantitation of SeMet in selenized yeast demonstrating its efficient use as an alternative method to other traditional chromatographic techniques.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".