Rapid sample preparation procedure for As speciation in food samples by LC-ICP-MS
Bibliographic record
Abstract
This paper describes a rapid method for arsenic (As) speciation by LC-ICP-MS in several types of food samples. Prior to analysis, samples were milled and the As species extracted from biological tissues by sonication in only 2 min with a solution containing MeOH (10%, v/v) plus HNO₃ (2%, v/v). As species were separated by LC using an anion exchange column. Method detection limits for AsB, As³⁺, DMA, MMA and As⁵⁺ were 1.3, 0.9, 0.6, 0.7 and 0.8 ng g⁻¹, respectively. Method accuracy and precision were traceable to Certified Reference Materials SRM1577 bovine liver from the National Institute of Standards and Technology, CE278 mussel tissue from the Institute of Reference Materials and Measurements and DOLT-3 dogfish liver tissue and DORM-3 fish protein from the National Research Council of Canada. Finally, the method was applied to speciate As in food samples (egg, fish muscle, beef and chicken) purchased in Brazilian markets.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".