3D/4D printed super reconstructed foods: Characteristics, research progress, and prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it