Optimization of the nutrient content and protein quality of cereal-legume blends for use as complementary foods in Ghana.

Abstract

Nutritionally adequate complementary foods made from locally available ingredients are of high priority in developing countries, including Ghana. The majority of complementary foods in these countries are cereal-based and are unable to meet the nutrient intakes recommended by the World Health Organization. To evaluate the nutrient content and protein quality of local cereal-legume blends for complementary foods against recommendations and to determine the quantities of additional ingredients required to meet needs by using linear programming. Nine cereal-legume combinations (maize, sorghum, or millet combined with cowpea, peanut, or soybean) and koko (a traditional Ghanaian maize-based complementary food) were evaluated based on the macronutrient targets for a daily ration of complementary food for the age group 12 to 24 months: 264 kcal, 6.5 g of protein, and 8.2 to 11.7 g of fat. Protein quality was assessed by the Protein Digestibility Corrected Amino Acid Score (PDCAAS). Linear programming was then used to determine the amounts of additional oil, sugar, and lysine needed to meet macronutrient requirements. No traditional cereal-legume food met all complementary food macronutrient requirements on its own. Cereal-legume blends made with peanut or cowpeas were low in quality protein, while those with soybean were low in fat. Lysine was the limiting amino acid (PDCAAS 0.50 to 0.82) in all blends. Adding lysine increased utilizable protein by 1% to 10% in soybean blends, 35% to 40% in peanut blends, and 14% to 24% in cowpea blends. Peanut-maize, peanut-millet, and all soybean-cereal blends were able to meet macronutrient targets; most micronutrients remained below recommended levels. Traditional cereal-legume blends made from locally available ingredients do not meet energy, quality protein, and fat recommendations for complementary foods; however, such complementary food blends may be optimized to meet nutrient requirements by using linear programming as a tool to determine the exact levels of fortificants to be added (including, but not limited to, added fat, amino acids, and micronutrients).