3D Food Printing Technology: A Critical Scientometric and Systematic Review, and Future Research Directions
Bibliographic record
Abstract
This paper presents a scientometric analysis and systematic literature review on 3D food printing. 3D food printing offers a huge potential to revolutionize the food industry by enabling personalized and customizable food. More specifically, food can be designed and produced in precise shapes, textures, and flavors, offering new opportunities for creative food design and innovation. Furthermore, 3D food printing can contribute to a more sustainable food production by reducing food waste and energy consumption. Since this technology has been increasingly explored in the last decade as a new and innovative approach to food production, we conducted a scientometric search and analysis on Scopus database. Additionally, we analyzed relevant literature through systematic literature review method aiming at drawing a comprehensive review on techniques, materials and material development, and technology applications. Our findings revealed that the majority of the research has been conducted in material development, followed by food characterization. We have identified several research gaps and future directions of 3D food printing such as limited printable materials and food safety concerns. Moreover, our study explored barriers such as social acceptance and advancements such as the application of artificial intelligence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.119 | 0.325 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.126 | 0.080 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".