Published May 2, 2023 | Version v1
Dataset Open

DIETxPOSOME - Summary statistics from papers obtained from literature mining and machine learning protocols

Authors/Creators

  • 1. LAQV/REQUIMTE, Departamento de Ciências Químicas, Laboratório de Bromatologia e Hidrologia, Faculdade de Farmácia, Universidade do Porto, Rua de Jorge Viterbo Ferreira n.º 228, 4050-313 Porto, Portugal
  • 2. Instituto Nacional de Saúde Dr. Ricardo Jorge, Departamento de Saúde Ambiental, Rua Alexandre Herculano 321, 4000-055 Porto, Portugal; LAQV/REQUIMTE, Universidade do Porto, Portugal
  • 3. Faculdade de Ciências da Nutrição e Alimentação, Universidade do Porto, Rua do Campo Alegre 823, 4150-180 Porto, Portugal

Description

DIETxPOSOME database concerning literature selection of potentially useful papers retrieved from PubMed search API, concerning contaminants quantification in food items of worldwide highest supply and using FoodMine code (text matching filter) and machine learning (ML) protocols. 11,723 data points were collected from 254 papers from the last two decades 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).

 

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Additional details

Funding

European Commission
ERA-HDHL - ERA-NET Biomarkers for Nutrition and Health implementing the JPI HDHL objectives 696295
Fundação para a Ciência e Tecnologia
UIDB/00006/2020 - Centre of Statistics and its Applications UIDB/00006/2020
Fundação para a Ciência e Tecnologia
PTDC/SAU-NUT/6061/2020 - Explore the impact of DIETary-eXPOSOME on chronic inflammation assessed through in vitro assays and mathematical modelling PTDC/SAU-NUT/6061/2020