Minimising fat in daily food intake using linear programming

This study aimed to minimise fat intake in the daily food intake of women aged 18 to 25 in Malaysia while still meeting their nutrient requirements. Using linear programming, the study focused on four food groups and 88 food items to determine the optimal daily fat intake for women. The study identified "good" unsaturated fats as important for preventing serious health consequences and categorised fats into four primary types: saturated, trans, monounsaturated, and polyunsaturated. However, some food items in the food composition database did not list nutrient values for specific types of fat, making it challenging to distinguish fat types in consumed food. The study utilised binary linear programming and Excel Solver to obtain optimal solution results on three constraints involved in the study. The solution approach involved selecting the best food item based on the food group that satisfies eight nutrient requirements intake. The selected foods included Naan bread for carbohydrates, chestnut, raw, chicken satay, cockles boiled and chicken fillet sandwich for protein. For fruits or veggies, cooked green peas with salt were chosen, and for a complete meal, nasi dagang, spaghetti with cheese and meat sauce, and fried kuetiau were selected. The study successfully identified an optimal solution that resulted in a daily fat intake of 27.46 g while still meeting nutrient requirements. The study's findings suggest that individuals can reduce their fat intake while still maintaining a balanced diet by selecting the right foods from different food groups. The study's results could be useful for healthcare professionals and individuals seeking to reduce their fat intake for health reasons.