Trace Metals (Cd, Co, Cr, Cu, Hg, Ni, Pb, and Zn) in Food Supplements of Marine Origin
Bibliographic record
Abstract
ABSTRACT We determined the concentrations of Cd, Co, Cr, Cu, Hg, Ni, Pb, and Zn in dietary supplements of marine origin. Four supplement categories were studied; algae, coral, krill, and shark cartilage. A direct mercury analyzer was used for Hg determinations while acid digestions and ICP-AES were used for Cr analysis and ICP-MS for the other trace metals. Algae are the supplements showing the highest concentrations of Pb, Cr, and Ni with respective means of 1.6 mg Pb/kg dry weight (d.w.), 3.2 Cr mg/kg d.w., and 8.0 mg Ni/kg d.w. Krill supplements have the highest levels of Cd, Cu, and Zn with 0.65 mg Cd/kg d.w., 63 mg Cu/kg d.w., and 50 mg Zn/kg d.w., respectively. Shark cartilage supplements show the highest levels of Hg and Co with mean concentrations of 160 μg Hg/kg d.w. and 73 ± 51 μg Co/kg d.w., respectively. No samples in our study exceeded the provisional tolerable daily intakes set by Health Canada, the joint committee of the World Health Organization/Food and Agricultural Organization, or the U.S. Environmental Protection Agency. Nevertheless, Ni and Pb in algae and Hg in shark cartilage may end up contributing to a very significant portion of the allowable daily intake—leaving little room for normal intake through food consumption and other exposure pathways. Key Words: algaecoralkrillshark cartilagetrace metalsnutraceuticalsnatural health productsdietary supplements ACKNOWLEDGMENTS Thanks go to Carmen Donisa, Hélène Lalande, and Dominic Bélanger for their help with the analytical work. Financial support was provided by the Natural Sciences and Engineering Research Council of Canada. Notes †The maximum allowable daily intake for methylmercury is 0.1, albeit we have measured total Hg, for shark cartilage, we expect 95% of the mercury to be methylmercury (CitationKrystek and Ritsema 2005).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.007 | 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 teacher head, 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".