ReviewFood chemistry: X2026
In vitro digestion models in food chemistry: advancements, challenges, and applications in nutrient bioaccessibility and bioavailability.
Review in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
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Abstract
Modern food chemistry and nutrition research stresses digestion and bioavailability. In vitro digestive models are needed to reconstruct the human gastrointestinal (GI) system and study food-derived chemical release, transformation, and absorption. This review examines major in vitro digestion platforms, including static and dynamic systems, organ-on-chip devices, and 3D-bioprinted gut models. Integrating these models with absorption simulators such as Caco-2 cells and advanced analytical instruments (e.g., HPLC, LC-MS/MS, FTIR, NMR, and OMICS technologies) enables comprehensive profiling of digestion products. These models assess bioaccessibility and bioavailability of polyphenols, omega-3 fatty acids, and encapsulated nutraceuticals. Computer modeling and artificial intelligence (AI) increase prediction for high-throughput functional food performance screening. Though promising, current models lack homogeneity and microbial representation often does not fully replicate in vivo gut microbiota complexity. In vitro digestive models assist food scientists enhance products. Priorities include harmonizing INFOGEST protocols, improving physiological relevance, and customizing nutrition and clinical validation. These developments may strengthen the utility of in vitro models in precision nutrition and industrial applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.