Author

Jianghua Feng

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Review Open access Jul 2026

Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.

Artificial intelligence (AI) is transforming food-nutrition-health research by enabling pattern recognition in complex, high-dimensional datasets that traditional hypothesis-driven approaches cannot address. This review systematically synthesizes research progress of AI across the food-nutrition-health continuum from 2020 to 2025. By examining 181 systematic reviews through PRISMA-guided selection, we provide a comprehensive overview and prospects across four dimensions: technical foundation, application scenarios, existing challenges, and future prospects. We propose a tripartite framework comprising (1) a data layer enabling multisource fusion of food composition, health monitoring, and individual characteristic data; (2) a technological layer of nondestructive testing (spectroscopy, nuclear magnetic resonance [NMR], imaging); and (3) an algorithmic layer progressing from machine learning to deep learning architecture. Key applications include food component analysis and safety detection; nutrition-disease association modeling; pathogen identification; and personalized dietary intervention systems. Despite rapid progress, critical challenges persist, insufficient model generalization across populations, algorithmic opacity limiting clinical trust, data privacy vulnerabilities, and lack of standardized multi-omics integration protocols. Future directions emphasize multimodal fusion models, explainable artificial intelligence (XAI), federated learning for privacy-preserving collaboration, gene-guided precision nutrition, and development of intelligent wearable devices and functional food. This review provides a roadmap for transitioning from population-averaged guidelines to dynamic, individualized health optimization through AI-enabled food system.

Xinru Wu, Jianghua Feng · Journal of Food Science · 0 citations

Aug 2026

Cluster-Based 1H NMR Alignment with Joint Phase-Baseline Optimization: A Physically Constrained Pipeline for Metabolomics and Food Authentication.

Accurate alignment of one-dimensional 1H NMR spectra is a prerequisite for reliable metabolomics, but chemical shift variability, line shape asymmetry, and multiplet overlap continue to compromise conventional warping algorithms. In this study, we propose a fully automated, cluster-based alignment framework that enforces the physical constraints of scalar coupling. Within a single optimization loop, zero- and first-order phase parameters are refined, while the baseline is dynamically re-estimated. Peaks are extracted with a matched filter derived from an in-spectrum singlet, and multiplets are recognized by a jump-detection criterion applied to a cluster-distance vector. Optimal peak-to-peak correspondence is then established under coupling-constant, binomial-intensity and coherent chemical-shift-variability rules, and a shift-corrected spectrum is reconstructed by cubic-spline interpolation. Validation on simulated spectra exhibiting severe "crossing chemical-shift variability" demonstrates accurate recovery of multiplet patterns. When applied to 81 1H NMR spectra of Lycium barbarum L. from three geographical origins, PCA demonstrates improved alignment accuracy and enhanced variance interpretation and geographical group discrimination. It is implemented in open-source Python for vendor format import, with demonstrated applicability to metabolomics or food-quality workflows.

Fengji Liu, Chengcheng He, Zesen Tian et al. · Analytical Chemistry · 0 citations