Probabilistic cancer risk assessment from heavy metal exposure in iranian rice and pasta: a novel hybrid framework integrating INAA, ICP-AES, and ensemble machine learning
Mahdi Azad Marzabadi, Hassan Khalili, Reza Pourimani, Mahdi Khalili · 2026
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Description
Mahdi Azad Marzabadi and co-authors assess the cancer risk of eating rice and pasta that carry trace heavy metals in Iran. The team measured metal levels in nineteen food samples using two lab techniques, neutron activation and plasma emission analysis. They then fed the numbers into a standard lifetime cancer risk model.
On top of the lab data, they simulated 1,750 exposure cases to explore uncertainty. Machine learning models were used not to predict risk, which the formula already fixes, but to rank which inputs move the risk most. Length of exposure mattered most, followed by how much chromium the body absorbs and by pasta eating habits. The computed risks ranged from acceptable to unacceptable depending on how much of the food a person eats. The authors say blending hard lab measurements with learning-based sensitivity checks gives regulators a sturdier basis for food safety rules.
Catalog record
| CREATOR | Mahdi Azad Marzabadi, Hassan Khalili, Reza Pourimani, Mahdi Khalili |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |