Evaluation of Some Heavy Metals in Imported Chocolate and Candies Sold in Nigeria
Bibliographic record
Abstract
This study was initiated as a follow-up information on some impounded products chocolates and candies (Samples A, B, C and D) imported from overseas, and sold in Nigeria markets. This result as their failure to meets Nation Agency for food drug administration and control (NAFDAC) regulatory standard. Samples of these products were collected in five markets of each six states each from the six geographical zones: North-West (Bauchi), North-East (Kano), North-Central (Kogi), South-West (Lagos), South-East (Abia), and South-South (Rivers) of the country. In order to evaluate the quality of the products, the levels of some heavy metals (Cu, Mn, Fe, Ni, Zn, Ti and Cr) were evaluated in the samples using X-ray fluorescence (XRF). Concentrations of metals in both chocolates and candies ranged 3.0 – 4.2 mg/g for Cu, 40.0 – 55.7 mg/g for Mn, ND – 102.5 mg/g for Fe, ND – 305.0 mg/g for Ni, ND – 42.5 mg/g for Zn, ND – 23.8 mg/g for Ti, and ND – 10.8 mg/g for Cr. The results showed that that these metals are at higher levels in Samples A, B, C and D compared to other studies in candies and chocolates in Nigeria. Correlation analysis among metals revealed positive correlations, which indicates similar sources of these metals. Also, evaluation of dietary intake of these products daily revealed that Samples A (except for Cu), B and C are above the daily dietary recommended limit for all the studied metals in food. Thus a frequent intake of these contaminated products is likely to induce health effects arising largely from Cu, Mn, Fe, Ni and Zn.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".