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TRUVACE RECORD VERSION
record: TRV-2026-1171
version: 2
kind: revised
reason: Direction audit: filed as a problem but describes a gain.
timestamp: 2026-10-26T06:38:41.825515Z
status: published
lens: g_space
sector: lifestyle
headline: Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration
dek: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods. This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, fr…
gain_title: Multimodal machine learning coupling diet and microbiome data reached up to 95% diagnostic accuracy in identifying diet-responsive states and glycemic outcomes.
problem_title: (none)
trace_subject: (none)
gain_reading: Multimodal machine learning coupling diet and microbiome data reached up to 95% diagnostic accuracy in identifying diet-responsive states and glycemic outcomes.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods.

This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, from generic industrial applications to the computational modeling of physiological and biochemical phenomena. We specifically explore critical integrations of Physics-Informed Neural Networks (PINNs) with established data-driven methods to circumnavigate the epistemological limitations of entirely data-driven models within standardized frameworks ( e.g. , INFOGEST), assessing their use in simulating gastrointestinal mass transfer and dissolution kinetics to achieve predictive accuracies up to R 2 = 0.91 in complex protein digestibility matrices.
limitation: 
tag: Evidence-backed gain
key_points: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. | These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods. | This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, from generic industrial applications to the computational modeling of physiological and biochemical phenomena.
rundown: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods.

This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, from generic industrial applications to the computational modeling of physiological and biochemical phenomena. We specifically explore critical integrations of Physics-Informed Neural Networks (PINNs) with established data-driven methods to circumnavigate the epistemological limitations of entirely data-driven models within standardized frameworks ( e.g. , INFOGEST), assessing their use in simulating gastrointestinal mass transfer and dissolution kinetics to achieve predictive accuracies up to R 2 = 0.91 in complex protein digestibility matrices.
sources:
- peer_reviewed | Food & Function | https://doi.org/10.1039/d6fo02990f | 2026-09-21
prev: 871122727b89eebe27dfb03a1516a6f52d1f94098c42bfbddc365f305094816b

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8cdb2c4ed67c68fee9bfdf273470247de44f338e0f99fd1e62769315d38ae3cd

Previous version’s hash

871122727b89eebe27dfb03a1516a6f52d1f94098c42bfbddc365f305094816b