Current practices, challenges and new advances in the collection and use of food composition data for Africa

被引:1
|
作者
Pretorius, Beulah [1 ]
Muka, Junior M. [2 ]
Hulshof, Paul J. M. [3 ]
Schonfeldt, Hettie C. [4 ]
机构
[1] Univ Pretoria, Dept Anim Sci, Pretoria, South Africa
[2] Univ Arkansas Little Rock, Dept Informat Sci, Little Rock, AR USA
[3] Wageningen Univ, Div Human Nutr, Wageningen, Netherlands
[4] Univ Pretoria, ARUA Ctr Excellence Sustainable Food Syst, Dept Anim Sci, Pretoria, South Africa
基金
新加坡国家研究基金会; 英国经济与社会研究理事会;
关键词
food composition data; food security; information and communication technology (ICT) tools; machine learning; capacity building;
D O I
10.3389/fsufs.2023.1240734
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
High-quality food composition data are indispensable for improved decision-making in food security, health policy formulation, food labeling, diet formulation, agricultural policymaking, nutrition research, and many other nutrition-related activities. The optimisation of dietary patterns is a powerful tool to reduce the impact of malnutrition on a population's health and well-being. Many countries in resource-poor settings lack a framework for developing and managing food composition data appropriate for these purposes. In the article, an overview of available food composition tables in Africa and the origin, use and limitations of theses tables are discussed. It is important that those working on any nutrition-related activity for resource-poor settings understand the limitations of current food composition data. Production of high-quality data requires the harmonization and adoption of international standards and guidelines across Africa. Moreover, continuity in the production, compilation and management of high-quality food composition data is challenged by suboptimal capacity building in terms of organizational, institutional and legal framework development. In this perspective article, the authors deliberate on challenges with a focus on Africa, while discussing new advances in food composition activities. Opportunities (such as the Internet of Things (IoT), wearable devices, natural language processing (NLP) and other machine learning techniques) to improve existing resources must be more actively explored and supported.
引用
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页数:6
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