Artificial intelligence to detect unknown stimulants from scientific literature and media reports

Anand K. Gavai*, Yamine Bouzembrak, Leonieke M. van den Bulk, Ningjing Liu, Lennert F.D. van Overbeeke, Lukas J. van den Heuvel, Hans Mol, Hans J.P. Marvin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The world market for food supplements is large and is driven by the claims of these products to, for example, treat obesity, increase focus and alertness, decrease appetite, decrease the need for sleep or reduce impulsivity. The use of illegal compounds in food supplements is a continuous threat, certainly because these compounds and products have not been tested for safety by competent authorities. It is therefore of the utmost importance for the competent authorities to know when new products are being marketed and to warn users against potential health risks. In this study, an approach is presented to detect new and unknown stimulants in food supplements using machine learning. Twenty new stimulants were identified from two different data sources, namely scientific literature applying word embedding on > 2 million abstracts and articles from formal and social media on the world wide web using text mining. The results show that the developed approach may be suitable to detect “unknowns” in the emerging risk identification activities performed by the competent authorities, which is currently a major hurdle.

Original languageEnglish
Article number108360
JournalFood Control
Volume130
Early online date22 Jun 2021
DOIs
Publication statusPublished - 2021

Keywords

  • Emerging risk
  • Enhancers
  • MedISys
  • Social media
  • Stimulants
  • Text mining
  • Word embedding

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