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Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

  • Johanna C Gerdessen (a1), Olga W Souverein (a2), Pieter van ‘t Veer (a2) and Jeanne HM de Vries (a2)

Abstract

Objective

To support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.

Design

Selection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.

Results

The food lists generated by the MILP model have good performance in terms of length, coverage and R 2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R 2 was similar to that of the benchmark.

Conclusions

The results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

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Copyright

Corresponding author

*Corresponding author: Email joke.vanlemmen@wur.nl

References

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Supplementary materials

Gerdessen Supplementary Material
Appendix 1

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Supplementary materials

Gerdessen Supplementary Material
Appendix 2

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Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

  • Johanna C Gerdessen (a1), Olga W Souverein (a2), Pieter van ‘t Veer (a2) and Jeanne HM de Vries (a2)

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