Strategic choices for in vitro food digestion methodologies enabling food digestion design

消化(炼金术) 背景(考古学) 生化工程 合理设计 生物技术 脂类消化 计算机科学 计算生物学 生物 化学 工程类 生物化学 遗传学 古生物学 酶 脂肪酶 色谱法
作者
Dorine Duijsens,Katharina Pälchen,J.M. Guevara-Zambrano,S.H.E. Verkempinck,M.R. Infantes-Garcia,Marc Hendrickx,Ann Van Loey,Tara Grauwet
出处
期刊:Trends in Food Science and Technology [Elsevier BV]
卷期号:126: 61-72 被引量:46
标识
DOI:10.1016/j.tifs.2022.06.017
摘要

In the past decades, the great interest in food digestion research led to a wide array of in vitro digestion (IVD) methods. Each of these methods have the potential of providing specific valuable scientific insights in digestion mechanisms and the rational structural design of foods. This review paper outlines important transitions in recent IVD research and formulates important considerations relevant for the set-up of future in vitro experiments, especially in the context of rational food digestion design. Important transitions are discussed, including the importance of kinetic experiments for macronutrient digestion, the relevance of the transition towards more complex (semi)-dynamic digestion conditions, the shift from single nutrients in simplified systems towards real foods and meals, and the emerging trend to adapt methods to mimic the gastrointestinal (GI) tract of specific populations. Notwithstanding the recent shift towards more complex IVD methods, the possibilities and advantages of more simple digestion methods should not be overlooked for mechanistic understanding or for sample screening purposes. Since the information retrieved from a simulation experiment depends on the applied conditions, the appropriate in vitro protocol should be chosen depending on the research question. In this context, the harmonization of digestion methods such as the standardized INFOGEST protocols can play a notable role in food digestion research and the development of tailored foods for all different strata of the population. • Strategic choices to design relevant in vitro digestion experiments are outlined. • Selected IVD method affects generated data and the learning outcomes. • IVD research evolves from static to semi-dynamic and dynamic approaches. • Kinetic approaches reveal nutrient hydrolysis patterns. • Research focus shifts from simple systems to real foods and specific populations.
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