Levels of Minor and Trace Elements of Some Commercial Fruit Juices and Syrup Produced in Artisanal and Semi-Industrial Units in Benin Republic
Bibliographic record
Abstract
Fruit juices have been gaining interest in recent years for their contributions of minerals and other essential vitamins. But, with the development of intensive agriculture massively using pesticides and mineral fertilizers, the risk of contamination of these juices is high along the production chain. In this study, we evaluated the concentrations of arsenic       (As), cadmium (Cd), berrylium (Be), aluminium (Al), strontium (Sr), tin       (Sn), barium (Ba), mercury (Hg), thallium          (Tl), lead   (Pb), thorium (Th), uranium (U) for 92 commercial samples of pineapple juice, cocktail of pineapple and watermelon and pineapple syrup of Benin and France using Inductively Coupled Plasma-Source Mass Spectrometry (ICP-MS). The concentrations vary in the following ranges: As <QL at 39.3 ppb, Cd <QL at 0.7 ppb, Be <QL at 0.59 ppb, Al 26.4 ppb at 30620 ppb, Sr 130 ppb at 4049 ppb, Sn<QL at 43 ppb, Ba 42 ppb at 1582 ppb, Hg <QL at 31.7 ppb, Tl<QL at 21.3 ppb, Pb<QL at 608 ppb, Th<QL at 24.7 ppb ppb and U <QL at 1.04 ppb. Se, Ag and Pt have concentrations below the quantification limit (<QL). The levels of Ba, Hg and Pb exceeded the norms for some samples. The presence of aluminum, arsenic and cadmium in the tested samples of fruit juices can be toxic since they have no nutritional value, and hence may be treated as potential contaminants in these beverages. The classification of concentration levels of metallic contaminants reveals two main groups, the minor and the trace elements. These results make it possible to classify the contaminants in the decreasing order of the concentrations in the following way: Al> Sr> Ba> Pb> Sn> Tl> As> Th> Hg> Cd> Be> U. The average concentrations’ levels of trace elements are generally in accordance with the levels obtained for French pineapple juices chosen as reference, national and international standards for the quality of beverages.
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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 | 0.000 | 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".