Shaping the future of food: 3D-Printed personalized nutrition and sustainable production pathways
Bibliographic record
Abstract
This review explores the transformative impact of 3D food printing on personalized nutrition and sustainable food production. It provides an overview of foundational printing techniques, such as selective laser sintering, material extrusion, and binder jetting, and examines recent advancements including 4D-6D printing and AI integration. The paper is structured with primary objective of how 3D food printing is reshaping dietary practices by enabling personalized and functional foods, addressing specific health needs like low-sugar or gluten-free diets, creating customized, nutrient-rich foods tailored to individual health needs, ensuring precision in nutrient delivery and enhanced bioavailability. It integrates biometric data for personalized diets while reducing food waste. Technology enhances convenience, accessibility, and food design innovation. Key challenges are discussed, including technical limitations (print speed, material compatibility), as well as social, environmental, economic, and regulatory barriers. The review identifies gaps in current technology and emphasizes the need for more advanced techniques, sustainable materials, and supportive policies. It also explores how AI-driven innovations can overcome these challenges and enhance food production efficiency. Consumer acceptance and market potential are analyzed, with a focus on trends and growth opportunities. The review assesses the environmental benefits of 3D food printing, such as waste reduction and efficient resource use, contributing to more sustainable food production practices. The review’s novelty lies in its comprehensive integration of technological, sustainability, and consumer considerations. It identifies future research directions, discusses regulatory implications, and proposes strategies for integrating 3D food printing into mainstream food systems. By addressing existing research gaps and offering actionable steps, this paper outlines a roadmap for advancing personalized nutrition and sustainable food production through 3D food printing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".