Survey and risk assessment of trace elements in foods from Taiwan containing red mould rice (<i>Monascus</i>) by ICP-MS
Bibliographic record
Abstract
The concentrations of seven trace elements (As, Cd, Cr, Pb, Se, Cu and Zn) in 93 red mould rice (Monascus) food samples in Taipei, Taiwan, were determined by inductively coupled plasma-mass spectrometry (ICP-MS) after wet digestion. The results, calculated in mg kg(-1) (wet weight) for each sample, revealed the general scenario of food safety in Taiwan: As (0.005-12.04), Cd (<0.0005-2.22), Cr (0.014-6.95), Cu (0.012-8.70), Pb (0.001-0.64), Se (<0.001-1.29) and Zn (0.020-67.02). Three food samples were identified with As concentrations higher than regulatory limits: a dietary supplement sample and a seaweed sample with As concentrations that exceeded the limit of Taiwan's health food standard of 2 mg kg(-1), and a canned eel sample with an As concentration that exceeded the limit of Canada's fish standard of 3.5 mg kg(-1). This study suggests that the estimated intakes of these seven trace elements from the consumption of foods containing Monascus pose little risk, as the trace element contents in the majority of samples were lower than the permissible/tolerable intakes per week according to the guidelines recommended by the Food and Agricultural Organization/World Health Organization (FAO/WHO). Moreover, their concentrations in foods containing Monascus differ widely for different food varieties, suggesting that external contaminants and raw materials are the main sources of trace elements. This study shows that ICP-MS is a simple method proposed for the determination of As, Cd, Cr, Pb, Se, Cu, and Zn in foods containing Monascus.
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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 | 0.001 |
| 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".