Challenges and Prospects of Plant-Protein-Based 3D Printing
Bibliographic record
Abstract
Three-dimensional (3D) printing is a rapidly developing additive manufacturing technique consisting of the deposition of materials layer-by-layer to produce physical 3D structures. The technique offers unique opportunities to design and produce new products that cater to consumer experience and nutritional requirements. In the past two decades, a wide range of materials, especially plant-protein-based materials, have been documented for the development of personalized food owing to their nutritional and environmental benefits. Despite these benefits, 3D printing with plant-protein-based materials present significant challenges because there is a lack of a comprehensive study that takes into account the most relevant aspects of the processes involved in producing plant-protein-based printable items. This review takes into account the multi-dimensional aspects of processes that lead to the formulation of successful printable products which includes an understanding of rheological characteristics of plant proteins and 3D-printing parameters, as well as elucidating the appropriate concentration and structural hierarchy that are required to maintain stability of the substrate after printing. This review also highlighted the significant and most recent research on 3D food printing with a wide range of plant proteins. This review also suggests a future research direction of 3D printing with plant proteins.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".