Dried-droplet laser ablation ICP-MS of HPLC fractions for the determination of selenomethionine in yeast
Bibliographic record
Abstract
Offline coupling of high performance liquid chromatography (HPLC) to dried droplet laser ablation (LA) ICP-MS detection is described. The method is based on the offline spotting of HPLC fractions onto a substrate followed by LA introduction of the dried sample residues to an ICP-MS for elemental analysis. Quantitation of selenomethionine (SeMet) in yeast using species specific isotope dilution (ID) was achieved following digestion of samples in 4 M methanesulfonic acid and HPLC separation of species. Chromatographic fractions having retention times of 180–210 seconds were collected for each sample. Dried micro-droplets from each fraction were ablated from a polystyrene substrate and quantitated for SeMet. Concentrations of 3301 ± 18 and 3309 ± 24 μg g−1 (one standard deviation, n = 4) with RSDs of 0.55% and 0.73% were obtained based on measured 78Se/74Se and 82Se/74Se ratios, in good agreement with the values of 3309 ± 19 and 3305 ± 26 μg g−1 (one standard deviation, n = 4, RSDs of 0.58% and 0.79%), respectively, obtained by direct HPLC-ICP-MS analysis. The proposed method provides a satisfactory alternative technique for the quantitation of SeMet in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".