Microwave-assisted acid digestion protocol for the determination of methionine and selenomethionine in selenium-enriched yeast by species specific isotope dilution GC-MS
Bibliographic record
Abstract
A quantitative and fast microwave assisted protein digestion method is described for the simultaneous determination of methionine (Met) and selenomethionine (SeMet) in yeast. Extraction of Met and SeMet from the selenized yeast was performed in a focused microwave system using methanesulfonic acid (MSA). The effects of parameters such as extraction time, temperature, power and sample mass on the extraction efficiencies of Met and SeMet were investigated. Species specific isotope dilution (ID) calibration using 13C enriched Met and SeMet spikes was employed to obtain accurate results. Analytes were derivatized with methyl chloroformate and extracted into chloroform prior to species specific ID GC-MS analysis. Using a 20 minute extraction time at 165 °C and 6 ml of 4 M MSA was found to be efficient for both analytes based on a 50 mg sample mass. Under these conditions, concentrations of 5862 ± 32 and 3366 ± 60 μg g−1 (one standard deviation, n = 3) for Met and SeMet, respectively, were obtained in SELM-1 yeast certified reference material (CRM). The obtained results are in good agreement with the certified values of 5758 ± 277 and 3448 ± 146 μg g−1 (expanded uncertainty, k = 2). Compared to previous MSA reflux digestion, this newly proposed method offers dramatic reduction in extraction time from 8–16 hours of the conventional MSA reflux to 20 minutes by microwave extraction, significantly improving the sample throughput. Additionally, the microwave extraction is fully automated and uses 75% less reagent (MSA) than the conventional acid reflux setup. The developed method is suitable for quasi real time production monitoring of Met and SeMet in Se enriched yeast and other food products.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".