Abstract There is increasing importance of upcycling of low-opportunity-cost feed, food waste, and food processing byproducts into animal products, strongly increasing the variation in the nutritional quality of feed ingredients. Traditionally, feed ingredients are evaluated based on their measured extent of digestion. Increasing awareness that not only the total yield of nutrients, but also their absorption kinetics, affect their metabolic fate after absorption, and a growing body of evidence of complex interactions taking place inside the gastro-intestinal tract urges development of new approaches. In a recently developed approach (Schop, 2020), we propose a combination of in vitro methodology and dynamic modelling of the digestion process as an alternative to conventional feed ingredient evaluation, and made the first steps in the development of such a system. The digestion potential, evaluated in vitro, is considered as the true property of feed ingredients. Then, prediction of digesta transit, nutrient hydrolysis and absorption, following the intake of a complete feed, determines the extent to which the digestion potential of each ingredient is exploited. The dynamic, mechanistic model developed by Schop for growing pigs comprises 48 state variables representing dietary nutrients, hydrolysis products, endogenous components, and microbial biomass. Driving variables are ingested nutrients from feed ingredients, characterized in vitro (solubility, undegradable fraction, maximum rate of digestion). Passage of digesta is modelled as a function of nutrient solubility, diet viscosity and feed intake. The extent of protein digestion and extent and rate of starch digestion, but not absorption of amino acids, were adequately predicted by the model. Future efforts should focus on modelling digesta properties and transit, translation of in vitro digestion kinetic data and generating reliable in vivo data on nutrient absorption kinetics across feed ingredients. Schop, T.A. 2020 Modelling digestion kinetics in pigs. Predicting nutrient absorption based on diet and ingredient properties. PhD thesis, Wageningen University, Wageningen, NL.
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