Uranium and Fluoride Accumulation in Vegetable and Cereal Crops: A Review on Current Status and Crop-Wise Differences
Bibliographic record
Abstract
Uranium (U) and fluoride (F−) contamination in agricultural products, especially vegetable and cereal crops, has raised serious concerns about food safety and human health on a global scale. To date, numerous studies have reported U and F− contamination in vegetable and cereal crops at local scales, but the available information is dispersed, and crop-wise differences are lacking. This paper reviews the current status of knowledge on this subject by compiling relevant published literatures between 1983 and 2023 using databases such as Scopus, PubMed, Medline, ScienceDirect, and Google Scholar. Based on the median values, F− levels ranged from 0.5 to 177 mg/kg, with higher concentrations in non-leafy vegetables, such as Indian squash “Praecitrullus fistulosus” (177 mg/kg) and cucumber “Cucumis sativus” (96.25 mg/kg). For leafy vegetables, the maximum levels were recorded in bathua “Chenopodium album” (72.01 mg/kg) and mint “Mentha arvensis” (44.34 mg/kg), where more than 50% of the vegetable varieties had concentrations of >4 mg/kg. The concentration of U ranged from 0.01 to 17.28 mg/kg; tubers and peels of non-leafy vegetables, particularly radishes “Raphanus sativus” (1.15 mg/kg) and cucumber “Cucumis sativus” (0.42 mg/kg), contained higher levels. These crops have the potential to form organometallic complexes with U, resulting in more severe threats to human health. For cereal crops (based on median values), the maximum F− level was found in bajra “Pennisetum glaucum” (15.18 mg/kg), followed by chana “Cicer arietinum” (7.8 mg/kg) and split green gram “Vigna mungo” (4.14 mg/kg), while the maximum accumulation of U was recorded for barley “Hordeum vulgare” (2.89 mg/kg), followed by split green gram “Vigna mungo” (0.45 mg/kg). There are significant differences in U and F− concentrations in either crop type based on individual studies or countries. These differences can be explained mainly due to changes in geogenic and anthropogenic factors, thereby making policy decisions related to health and intake difficult at even small spatial scales. Methodologies for comprehensive regional—or larger—policy scales will require further research and should include strategies to restrict crop intake in specified “hot spots”.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".