engleski

Fuzzy logic modelling in nutrition planning-application on meals in boarding schools

Fuzzy logic modelling is applied for modelling and optimisation of nutritional requirements. According to the theory of fuzzy logic, this method is applicable to systems which cannot be precisely defined, such as human nutrient requirements. Fuzzy membership functions are modelled to describe the range of nutrient intakes in the range from deficient to excess amounts. Defined are 23 fuzzy sets: fuzzy set for cost of a daily menu, fuzzy set of preference choice of offered meals and 21 fuzzy sets for amounts of energy and nutrients. Developed is a computer program in Wolfram Research Mathematica for modelling and optimisation of nutrient and energy intake. A measurement of appropriate energy and nutrient intake with a respect to the recommendations, or optimal intake, is evaluated by Prerow value (PV). PV value was used to evaluate the efficiency of meal combinations concerning the daily costs, meal preferences and the amount of energy and nutrients. Presented are experimentally determined deviations of energy and nutrients intakes from the recommendations. Adjustments needed for improvement of nutrition requirements corresponding to linguistic variable were determined by use of fuzzy sets without major change of the menus. The method is applied for menu planning in Croatian boarding schools where female and male students aged from 14 to 19 years are accommodated. At the same time the economic and meal preference aspects of meal planning are incorporated. Preferences are based on the fact that in different regions of Croatia nutrition habits are greatly different. The preferences for offered meals are included in the complete daily menu evaluation. This study shows that the use of fuzzy sets can be used for Pareto optimisation by which multiple object optimisation is achieved. The fuzzy model represents recommended energy and nutrient intake more adequately then the Dietary Recommended Intake, DRI, intake presented as crisp values, as well as to obtain acceptable price and preferences of menu selection for a population group.

fuzzy sets, optimisation, nourishment, meal planning

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