A Systematic Review of Linear Programming Techniques as Applied to Diet Optimisation and Opportunities for Improvement

Table 4

Summary of details retrieved from articles that maximised objective function.

Reference	Objective function (s)	Decision variable (s)	Constraint (s)	Focus	Mathematical approach used
Rambeloson et al. [51]	Maximum nutritional requirement	Edible grams of the selected foods	Nutrient	Investigate the nutritional quality of FB food aid delivered in France	Linear programming
Wilson et al. [35]	Maximum nutritional requirement	The amount of food	Nutrient, cost	A dietary pattern that met the essential nutritional requirements for New Zealand men	Linear programming
van Dooren et al. [52]	Maximise most consumed foods	Price of food, amount of food	Energy and nutrient, cost, GHGE	Find low-priced diets with low climate impact yet fulfil all nutritional needs	Linear programming
dos Santos et al. [7]	Maximum nutritional requirement		Nutrient deviations from observed dietary patterns	A diet that does not vary much from the observed dietary pattern	Linear programming
Morrison et al. [41]	Maximise the iron content of food	Quantity of foods	Nutrient constraint, intake constraint (median portion size, upper and lower bounds on frequency)	Develop strategies to address anaemia in pregnancy in rural plains in Nepal	Linear programming
van Wonderen et al. [53]	Maximise the iron content of diets	Quantity of foods	Constraints on DRIs	Plan a 2-week menu to estimate the iron bioavailability of different diets for women of reproductive age	Mixed integer linear programming

GHGE—greenhouse gas emission, FB—food basket, and DRIs—dietary recommended intakes.