Arsenic speciation in cattail (<i>Typha latifolia</i>) using chromatography and mass spectrometry
Bibliographic record
Abstract
Typha latifolia, commonly known as cattail, is widely used as traditional food and medicinal ingredients by indigenous people. There have been concerns over the high levels of total arsenic in cattail plants, but the chemical species of arsenic in cattail have not been characterized. We describe here the determination of arsenic species in the various compartments of cattail. Average concentrations of total arsenic from 9 to 19 cattail plants were 1120 microg/kg (range 68-2600 microg/kg) in the fine (hairy) roots, 575 microg/kg (range 16-1400) in the skin of tuber, 26 microg/kg (range 2-82) in the core of the tuber, 6 microg/kg (range 5-12) in the stem, and 420 microg/kg (range 4-1970) in the whole tuber. Speciation analysis using strong anion exchange, ion pairing, and strong cation exchange chromatography separation with MS detection revealed the presence of inorganic arsenite, arsenate, dimethylarsinic acid, and monomethylarsonic acid. The two inorganic arsenic species accounted for >80% of the total arsenic. Further analyses of arsenic and iron concentrations showed a strong correlation between arsenic and iron in the fine roots and skin. These results suggest that arsenic and iron are colocalized (codeposited) in the skin of the cattail plants, consistent with the previous findings. The level of exposure to arsenic from the use of cattail as food and medicine can be substantially reduced by removing the skin of cattail.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".