Evaluation of sample preparation procedures and krypton as an interference standard probe for arsenic speciation by HPLC-ICP-QMS
Bibliographic record
Abstract
Different conditions of extraction using water, a methanol–water mixture and nitric acid solutions were evaluated for speciation of As(III), As(V), DMA and MMA in plant samples that previously received As(V) after being sown and emergence was investigated. Microwave-assisted extraction (MAE) using diluted nitric acid solutions was also performed for arsenic extraction from chicken feed samples. The separation and determination of arsenic species were performed using HPLC-ICP-MS. The interference standard method (IFS) using 83Kr+ as the IFS probe was employed to minimize spectral interferences caused by polyatomic species, such as 40Ar35Cl+. The extraction procedures tested presented adequate extraction efficiencies (90%), and the four arsenic species evaluated were found in plant samples. Extractions with diluted nitric acid solution at 90 °C were the most efficient strategy, with quantitative recoveries for all four As species in plant tissues. On the other hand, the methanol–water mixture was the solvent with the lowest extraction efficiency (50–60%). For chicken feed samples, MAE at 100 °C for 30 min resulted in an extraction efficiency of 97% and only As(V) was found, without any species interconversion. The IFS method contributed to improving precision and limits of detection and quantification for all tested extraction procedures. Significant improvements on accuracy were obtained by applying the IFS method and recoveries between 77 and 94%, and 82 and 93% were obtained for plant extracts and chicken feed samples, respectively.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".