{
  "id": 1664133,
  "title": "Health risk assessment of heavy metals in Iranian cereal products using ICP-AES and a hybrid machine learning framework",
  "url": "https://urgent.news/2026/08/18/health-risk-assessment-of-heavy-metals-in-iranian-cereal-products",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T00:00:00.000Z",
  "source": {
    "name": "Scientific Reports",
    "slug": "scientific-reports",
    "url": "https://www.nature.com/articles/s41598-026-67271-w"
  },
  "original_language": "en",
  "account": "This research examines the health risks posed by heavy metal contamination in 13 popular cereal-based products from Arak, Iran. The team measured arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), and copper (Cu) levels using inductively coupled plasma atomic emission spectrometry (ICP-AES). Quantification revealed ranges from 0–0.834 mg/kg for As, 0.0002–0.014 mg/kg for Cd, 0–0.502 mg/kg for Hg, 0–2.0 mg/kg for Pb, and 0–1.203 mg/kg for Cu. The highest hazard quotients (HQ) were identified for Tak (HQ = 1.657) and Zhik (HQ = 0.928) brands, primarily due to mercury (Hg) and arsenic (As) respectively, suggesting potential non-carcinogenic health risks. In comparison, Zar, Mak, and Jahan brands had HQ values under 0.01. Researchers created a hybrid machine learning framework, merging Random Forest and Gradient Boosting, to predict HQ. The model demonstrated high accuracy (R2 = 0.97, RMSE = 0.042) and highlighted mercury (34%), arsenic (28%), copper (16%), cadmium (12%), and lead (10%) as the primary risk factors. While the study's small sample size (n=13) necessitates further investigation, the hybrid ensemble approach proves to be a reliable and efficient method for rapid food safety assessment. Funding was provided by the Research Council of Arak University.",
  "summary": "Scientific Reports, Published online: 18 August 2026; doi:10.1038/s41598-026-67271-w Health risk assessment of heavy metals in Iranian cereal products using ICP-AES and a hybrid machine learning framework",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}