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

Table 3

Summary of details retrieved from articles that minimised environmental factors.

Reference	Objective function (s)	Decision variable (s)	Constraint (s)	Focus	Mathematical approach used
Colombo et al. [48]	Reduce GHGE	Amount of food	Nutrition, affordability, cultural	The model that had lower GHGE levels and was nutritionally adequate and affordable	Linear programming
Larrea-Gallegos and Vázquez-Rowe [49]	Minimise the total amount of GHGE of optimised diets	Amount of food, carbon emission for the foods	Nutritional, environment, and quantity of foods		Linear programming with the integration of Monte Carlo simulation
Ferrari et al. [50]	Minimise GHGE while satisfying nutritional (RDIs), acceptability, and health constraint	Amount of food	Nutritional (nutrient and energy), acceptability (mean total amount of food and beverage that was constrained between 80 and 140% of observed intake and percentile on calculated mean observed diet), and healthy constraints (established lower and upper limit)	To create a sustainable and healthy Italian diet with low GHGE that meets dietary requirements and reflects current food intake patterns	Linear programming
Tompa et al. [40]	Reduced dietary water footprint and minimise the deviation between observed and model diets	Weight of subgroups	Constraints on nutrient requirements, cultural acceptability, and stepwise environmental reduction	Design sustainable diets that minimise dietary water footprint in Hungary	Linear programming

GHGE—greenhouse gas emission; RDIs—recommended dietary intakes.