From data to insight: Exploring contaminants in different food groups with literature mining and machine learning techniques.

ABSTRACT: Food remains a major source of human exposure to chemical contaminants that are unintentionally present in commodities globally, despite strict regulation. Scientific literature is a valuable source of quantification data on those contaminants in various foods, but manually summarizing the information is not practicable. In this review, literature mining and machine learning techniques were applied in 72 foods to obtain relevant information on 96 contaminants, including heavy metals, polychlorinated biphenyls, dioxins, furans, polycyclic aromatic hydrocarbons (PAHs), pesticides, mycotoxins, and heterocyclic aromatic amines (HAAs). The 11,723 data points collected from 254 papers from the last two decades were then used to identify the patterns of contaminants distribution. Considering cont

SUBMITTER: Martins ZE 

PROVIDER: S-EPMC10432907 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature