Simultaneous Speciation Analysis of Arsenic, Chromium, and Selenium in the Bioaccessible Fraction for Realistic Risk Assessment of Food Safety
Bibliographic record
Abstract
A simple and fast method was developed for risk assessment of As, Cr, and Se in food, which is demonstrated here using three cooked and uncooked rice samples (basmati as well as organic white and brown rice). The bioaccessible fraction was first determined through online leaching of rice minicolumns (maintained at 37 °C) sequentially with artificial saliva, gastric juice, and intestinal juice while continuously monitoring potentially toxic elements (As, Cr, and Se) by inductively coupled plasma mass spectrometry (ICPMS). Then, a new ion chromatography method with online detection by ICPMS was developed for the simultaneous speciation analysis of As, Cr, and Se in the bioaccessible fraction to determine the portion of these elements that was actually toxic. Using gradient elution, four As species [As(III), As(V), monomethylarsonic acid, and dimethylarsinic acid], two Cr species [Cr(III) and Cr(VI)], and two Se species [Se(IV) and Se(VI)] were separated within 12 min. The simultaneous speciation analysis of As, Cr, and Se revealed that the simple act of cooking can convert all of the carcinogenic Cr(VI) to the safer Cr(III).
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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) | 0.000 | 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".