Exposure Assessment of Essential and Potentially Toxic Metals in Wheat-Based Sweets for Human Consumption: Multivariate Analysis and Risk Evaluation Studies
Bibliographic record
Abstract
In recent years, there has been a growing concern about the negative impact of unforeseen contaminants such as metals in commonly consumed food items, which pose a threat to human well-being. Therefore, it is of utmost importance to evaluate the levels of these contaminants to guarantee the safe consumption of these food items. The goal of the current research is to determine the levels of essential (EMs: Mg, Ca, Mn, Fe, Co, Cu, and Zn) and potentially toxic metals (PTMs: Al, Cr, Ni, As, Cd, and Pb) in various brands of wheat-based sweets. One hundred samples were collected and analysed via flame atomic absorption spectrometry (FAAS) and inductively coupled plasma-optical emission spectrometry (ICP-OES). Also, the current study was to investigate the distribution, correlation, and multivariate analysis of 13 metals (Mg, Ca, Mn, Fe, Co, Cu, Zn, Al, Cr, Ni, As, Cd, and Pb). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to interpret the metals' association. The concentration (mg/kg) ranges of EMs were, in order, Mg (12.70-65.67), Ca (24.02-209.12), Mn (1.32-9.61), Fe (4.55-111.23), Co (0.32-8.94), Cu (2.12-8.61), and Zn (2.60-19.36), while the concentration (mg/kg) ranges of PTMs were, in order, Al (0.32-0.87), Cr (0.17-5.74), Ni (0.36-1.54), Cd (0.16-0.56), and Pb (0.14-0.92), and As was not detected in any sample under investigation. The HCA data revealed that Co, Al, and Ni form clusters with other metals. Sweets are prepared at high temperatures, and the elevated temperatures can increase the likelihood of Ni and Al leaching from stainless steel. Tolerable dietary intake (TDI) values for Ni were higher than the values established by the European Food Safety Authority (EFSA). The CR value found for the Ni and Cr was at the threshold level of cancer risk, if an amount of 25 g were to be used over a lifetime. In a nutshell, this study highlights the monitoring of EM and PTM levels in wheat-based sweets, and from a food safety perspective, the study is important for consumers of wheat-based sweets.
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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 | 0.002 | 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 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".