ArticleJournal of food science2026
AI Digitized 3D-Printed Meal for Personalized Nutrition.
Article in Journal of food science, 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
7 authors.
Funding
Abstract
Personalized nutrition requires practical methods to convert dietary assessments into tailored food portions. Current image-based assessment tools and 3D food printing systems are often disconnected, lacking end-to-end evaluation. This study presents an integrated platform that converts meal images into personalized 3D-printed macronutrient supplements. The system combines smartphone image analysis, volume calculation, nutrient recommendation, and automated G-code generation for a custom dual-nozzle 3D printer. Four nutrient-rich inks (avocado, purple sweet potato, red lentil, chicken breast) were formulated with guar gum and characterized for extrusion-based fabrication. The AI dietary assessment module achieved a mean volume and weight estimation error of 37.25%, with macronutrient estimation errors between 31% and 38%. Ablation studies demonstrated that user-calibrated 3D depth reconstruction reduced volume errors compared to 2D baselines and uncalibrated models. During fabrication, the optimized inks exhibited shear-thinning behavior and elastic-dominant responses, enabling stable extrusion. Printed shape fidelity ranged from 89.67% to 99.00%, and weight accuracy from 79.00% to 96.00%. These results demonstrate the feasibility of a closed-loop workflow linking AI dietary assessment with automated, on-demand food fabrication, paving the way for precision nutrition interventions.
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Registered trials
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 OpenQuestion graph.