A Data Driven Approach to Identify Safe and Adequate Schemes for Vitamin D Fortification

Tue Christensen*, Gitte Ravn-Haren, Rikke Andersen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Food fortification is a strategy to increase low vitamin D intake. In order to avoid the intake of a population exceeding the upper tolerable intake level, the right choice of food groups to fortify is of crucial importance. An automated fortification tool was developed based on dietary intake data from the Danish National Survey of Dietary Habits and Physical Activity 2011–2013 (DANSDA), taking into account the energy contribution of the fortified food. The fortification of food group is a variant in the linear modelling, where the optimization ensures the lowest possible variation in deviation of the calculated intake and the target intake. The resulting tool demonstrated that the lowest limit of fortification, where the model works, is 12 µg/10 MJ, when fortification of any food group is allowed. The tool also demonstrated that, by increasing the allowed upper level of fortification from 12 µg/10 MJ up to 30 µg/10 MJ, the food groups selected for fortification and the level of fortification in those food groups may change. Specifically, fewer food groups seem to be needed as the upper level of fortification is increased. The optimized scenarios, using the food groups, including milk, cheese, cereals, fats, and juice, were tested on dietary-survey data and demonstrated that all the projected scenarios manage to lift the median vitamin D intake to the targeted intake safely. A data-driven approach was used to develop a simple, fast, and automated fortification tool to test different vitamin D food fortification strategies.
Original languageEnglish
Article number3981
Issue number24
Number of pages11
Publication statusPublished - 2022


  • Fortification
  • Vitamin D
  • Food
  • Dietary-intake data
  • Optimization tool
  • Fortification strategy
  • Linear modelling


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