Potential health risk and bio-accessibility of metal and minerals in saltpetre (a food additive)
Bibliographic record
Abstract
Additives are added to food to improve its safety, appearance, taste, texture and freshness. Heavy metals in diet can affect metabolic processes with possible detrimental health effects to humans depending on the absorbed dose, route and duration of exposure. In this study, heavy metal content of saltpetre (a food additive containing predominantly potassium nitrate), was quantified using Niton Thermo scientific X-Ray Fluorescence Analyser, XRF (Mobile Test S, NDTr-XL3t-86956, com 24). High concentrations of both essential metals (Ca, K, Fe, Zn) and toxic metals (As, Pb) were present in the saltpetre samples, which were purchased from selected markets in the Kumasi Municipality of Ghana. Mercury and cadmium were not detected. The exposure, health risk and bio-accessibility studies established Arsenic as a major contributor to potential heath related disorders. This research creates awareness on the need to monitor the heavy metal concentrations in saltpetre and the possible health implications to consumers.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".