Multi-elemental bio-accessibility from long-grain rice for realistic risk assessment using on-line continuous leaching coupled to inductively coupled plasma mass spectrometry: The non‑arsenic side of the story
Bibliographic record
Abstract
This study investigates the leaching of toxic (Cd and Pb) and essential elements (Cu, Fe and Zn) from organic white, organic brown and basmati rice using the continuous on-line leaching method and a conventional batch method. The samples were maintained at 37 °C while being sequentially leached by artificial saliva, gastric juice and intestinal juice. Elements released were determined by inductively coupled plasma mass spectrometry, revealing over 60 % bio-accessibility in most instances. The total concentrations of Cd (140–150 μg kg −1 ) and Pb (150–170 μg kg −1 ) are near or exceed regulations in Europe. Washing rice prior to cooking reduced the toxic elements concentration by up to 50 % while preserving essential elements. However, consuming less than half a serving could still pose a health risk to a 20-kg child. Correlations between temporal leaching profiles of different elements revealed common sources of those elements, which differed between rice types, potentially enabling rice discrimination.
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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 | 0.000 | 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".