3D/4D printed super reconstructed foods: Characteristics, research progress, and prospects
Bibliographic record
Abstract
Super reconstructed foods (SRFs) have characteristics beyond those of real system in terms of nutrition, texture, appearance, and other properties. As 3D/4D food printing technology continues to be improved in recent years, this layered manufacturing/additive manufacturing preparation technology based on food reconstruction has made it possible to continuously develop large-scale manufacture of SRFs. Compared with the traditional reconstructed foods, SRFs prepared using 3D/4D printing technologies are discussed comprehensively in this review. To meet the requirements of customers in terms of nutrition or other characteristics, multi-processing technologies are being combined with 3D/4D printing. Aspects of printing inks, product quality parameters, and recent progress in SRFs based on 3D/4D printing are assessed systematically and discussed critically. The potential for 3D/4D printed SRFs and the need for further research and developments in this area are presented and discussed critically. In addition to the natural materials which were initially suitable for 3D/4D printing, other derivative components have already been applied, which include hydrogels, polysaccharide-based materials, protein-based materials, and smart materials with distinctive characteristics. SRFs based on 3D/4D printing can retain the characteristics of deconstruction and reconstruction while also exhibiting quality parameters beyond those of the original material systems, such as variable rheological properties, on-demand texture, essential printability, improved microstructure, improved nutrition, and more appealing appearance. SRFs with 3D/4D printing are already widely used in foods such as simulated foods, staple foods, fermented foods, foods for people with special dietary needs, and foods made from food processingbyproducts.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".