Linear programming for instant complimentary food formulations among Tanzanian infants aged 6 to 23 months

Document Type : Original Article

Authors

1 Department of Mathematics, Faculty of Science, Muslim University of Morogoro, Morogoro, Tanzania

2 Department of Public Health and Community Nursing, University of Dodoma

Abstract

It is challenging to follow all nutritional requirements simultaneously. A good mathematical tool for converting nutrient-based suggestions into realistically nutritionally ideal food combinations integrating locally accessible foods is the diet optimization model. The objective of this study is to design a linear programming model that figures out how many grams of each food type need to be mixed to produce an instant meal complement for infants between the ages of 6 and 23 months. The mathematical model developed computes the grams of each food type – Quelea mixed with either Green Banana or White Rice or Irish Potato and Onions, Tomatoes, Carrots and Green bell Pepper. When those foods were combined, an instant food complement will be created and entirely satisfy the preset needs of malnourished infants. Thus, Tanzanian public health technologists and nutritionists may apply the linear programming approach explored in this study to create new ready-to-use food formulations.

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Main Subjects


[1] R. Aravindhakshan, Optimized diet design using linear programming, Pushpagiri Med. J., 2(2) 2011.
[2] G. Baldi et al., Cost of the Diet (CoD) tool: first results from Indonesia and applications for policy discussion on food and nutrition security., Food Nutr. Bull., 34(2) 2013.
[3] A. Briend, N. Darmon, E. Ferguson, J. G. Erhardt, Linear programming: A mathematical tool for analyzing and optimizing children’s diets during the complementary feeding period, Journal of Pediatric Gastroenterology and Nutrition, 36(1) 2003.
[4] A. Briend, E. Ferguson, N. Darmon, Local food price analysis by linear programming: A new approach to assess the economic value of fortified food supplements, Food Nutr. Bull., 22(2) 2001.
[5] G. Brixi, Innovative optimization of ready to use food for treatment of acute malnutrition, Matern. Child Nutr., 14(4) 2018.
[6] N. Darmon, F. Vieux, M. Maillot, J.L. Volatier, A. Martin, Nutrient profiles discriminate between foods according to their contribution to nutritionally adequate diets: A validation study using linear programming and the SAIN, LIM system, Am. J. Clin. Nutr., 89(4) 2009, 1227–1236.
[7] S. De Pee, M.W. Bloem, Current and potential role of specially formulated foods and food supplements for preventing malnutrition among 6-to 23-month-old children and for treating moderate malnutrition among 6-to 59-montn-old children, Food Nutr. Bull., 30(3) 2009.
[8] R. Gazan, C. M. C. Brouzes, F. Vieux, M. Maillot, A. Lluch, N. Darmon, Mathematical optimization to explore tomorrow’s sustainable diets: A narrative review, Advances in Nutrition, 9(5) 2018, 602–616.
[9] S.L. Huffman, E.G. Piwoz, S.A. Vosti, K.G. Dewey, Babies, soft drinks and snacks: a concern in low-and middle-income countries? Matern. Child Nutr., 10(4) 2014, 562–574.
[10] R. Jofrey, N. Kassim, W. R. Jerman, A. Morris, Optimal formulations of local foods to achieve nutritional adequacy for 6–23-month-old rural Tanzanian children, FOOD Nutr. Res., 61 2017.
[11] M. Maillot, E. L. Ferguson, A. Drewnowski, N. Darmon, Nutrient profiling can help identify foods of good nutritional quality for their price: A validation study with linear programming, J. Nutr., 138(6) 2008, 1107–1113.
[12] M. Maillot, F. Vieux, E.F. Ferguson, J.L. Volatier, M.J. Amiot, N. Darmon, To meet nutrient recommendations, most French adults need to expand their habitual food repertoire, J. Nutr., 139(9) 2009.
[13] M. Manary, M. Callaghan, L. Singh, A. Briend, Protein Quality and Growth in Malnourished Children, Food Nutr. Bull., 37 2016, S29–S36.
[14] Ministry of Health, Tanzania National Nutrition Survey 2014. Final Report, Dar es Salaam, 2015.
[15] Ministry of Health, Tanzania National Nutrition Survey 2018: Final Report, Dar es Salaam, 2019.
[16] A. N. Patil, S. Kasturi, Optimal Diet Decision Using Linear Programming, Int. Res. J. Eng. Technol., 3(8) 2016, 2197–2199.
[17] O. Santika, U. Fahmida, E.L. Ferguson, Development of food-based complementary feeding recommendations for 9- To 11-month-old peri-urban indonesian infants using linear programming 1-2, J. Nutr., 139(1) 2009.
[18] B. Sawssan, Young American’s diet problem: A linear programing application, Int. J. Innov. Appl. Stud., 2021.
[19] R.P. Sen, Operations research: algorithms and applications, PHI Learn., 2010.
[20] M. Sheldon, K.M. Gans, R. Tai, T. George, E. Lawson, D.N. Pearlman, Availability, affordability, and accessibility of a healthful diet in a low-income community, central falls, rhode island, 2007-2008, Prev. Chronic Dis., 7(2) 2010.
[21] A. Tesha, C.N. Nyaruhucha, A.W. Mwanri, Formulation and Sensory Evaluation of Complementary Foods from Low-Cost, Locally-Available and Nutrient-Dense Ingredients using Linear Programming, Tanzania J. Agric. Sci., 20(2) 2021, 295–308.
[22] R. Uauy C. Castillo, Lipid requirements of infants: Implications for nutrient composition of fortified complementary foods, in Journal of Nutrition, 133(9) 2003.
[23] B.S. Vitta, M. Benjamin, A.M. Pries, M. Champeny, E. Zehner, S.L. Huffman, Infant and young child feeding practices among children under 2years of age and maternal exposure to infant and young child feeding messages and promotions in Dar es Salaam, Tanzania, Matern. Child Nutr., 12 2016.
[24] P.E. Wilde, J. Llobrera, Using the thrifty food plan to assess the cost of a nutritious diet, J. Consum. Aff., 43(2) 2009, 274–304.
[25] W.L. Winston, Operations Research: Applications and Algorithms, Forth Edition, 2004.
[26] A.H. Yepi’e, I. N’Goran David Vincent Kouakou, B.A. Bamba, O.S. Ake-Tano, A.L.A. Atchibri, Optimization of a local ingredient-based ready to use food using linear programming for the treatment of moderate acute malnutrition in Côte d’Ivoire, Int. J. Appl. Res., 2019, 93–100.
[27] W. Zhu, S. Zhu, B.F. Sunguya, J. Huang, Urban–rural disparities in the magnitude and determinants of stunting among children under five in tanzania: Based on tanzania demographic and health surveys 1991–2016, Int. J. Environ. Res. Public Health, 18(10) 2021.