Mercury speciation in Cuban commercial edible fish by HPLC-ICP-MS using the double spike isotope dilution analysis strategy
Bibliographic record
Abstract
A sensitive and accurate quantitative method for the speciation of inorganic Hg2+ and methyl-mercury by ID-HPLC–ICP-MS has been optimised and implemented for marine fish samples. Quantitative extraction of Hg species was achieved using a 0.1% (v/v) 2-mercaptoethanol, 0.05% (w/v) L-cysteine and 0.10% (v/v) HCl solution by sonication for 30 min. Chromatographic separation of mercury species was carried out on a C8 reverse phase column with 0.05% (v/v) 2-mercaptoethanol, 0.075% (w/v) L-cysteine and 0.06 mol L−1 ammonium acetate as the mobile phase. A species specific isotope dilution analysis approach, using 201CH3Hg+ and 200Hg2+ was employed for the quantification of both species. Two biological Certified Reference Materials (DOLT-2, DORM-2) were analysed to assess the analytical performance. No significant differences were found between the obtained concentrations and the certified reference values for total Hg and CH3Hg+. The results indicate that no species interconversion reactions occurred during the used extraction and chromatographic separation procedures. The detection limits for CH3Hg+ and Hg2+ species were 7.7 and 5.2 ng g−1, respectively. The method recovery (expressed as the sum of both species contents in relation to total Hg concentration analysed by ICP-MS) was about 97 ± 5%. The procedure was applied to speciation of mercury in 12 species of the most commonly consumed commercial fish in Cuba.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".