Determination of As, Sb, Se, Sn and Hg in beer and wort by direct hydride generation sample introduction−electrothermal AAS
Bibliographic record
Abstract
A method is described for the hydride generation atomic absorption (HG-AAS) determination of As, Sb, Se, Sn and Hg in untreated samples of beer and wort using a batch system and in situ preconcentration of the analytes onto the Pd- (for As, Sb, Se, Sn) or Au-pretreated (for Hg) interior wall surfaces of a graphite furnace. Samples were degassed by filtration and the hydride was generated in the presence of an antifoam agent. Instrument parameters, hydride generation, transportation and trapping were selected. Determination of the total concentration of these elements was obtained after a previous reduction with thiourea. To minimize the amount of moisture from the reaction vessel reaching the graphite tube, an extra gas–liquid separator was installed in line. For 10 mL of sample, detection limits (LOD, 3σblank, peak height) of 28, 21, 10, 50 and 90 ng L−1 were obtained for As, Sb, Se, Sn and Hg, respectively, reflecting overall generation/collection efficiencies of 53, 100, 54, 57 and 66%, respectively. The detection limits were restricted by variations in the blank absorbance. Precision of replicate determination was typically 5% RSD at a concentration 50-fold above the LOD for a 10 mL sample volume. The accuracy of the method was confirmed by comparing the results obtained with those found for beer and wort using microwave-assisted digestion and by analysing five certified reference materials. Data comparable with those obtained by hydride generation after microwave-assisted digestion of samples were found for a series of natural samples, giving evidence of the applicability of the developed methodology to directly determining analytes. Calibration was achieved via the method of standard additions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".