Perspectives, analyses, and progress in additive manufacturing of food
Bibliographic record
Abstract
Additive manufacturing techniques involve various steps and processes to create intricate shapes using edible or non-edible materials. This unique technology relies upon layer-by-layer operation to create an object that is pre-designed and coded with the help of specific software. The major challenges of three-dimensional printing that heavily depend on the material properties and machine capabilities are the ability to get a designed shape with high precision and accuracy, printing speed, and scalability. A critical review discussing the technology implementation from the perspectives of the food industry trends is of great interest, especially to industry professionals and academia. Adapting the technology in the food sector requires a critical view to overcoming technical barriers, which account for the food industry needs and current global challenges. In this regard, the relevant process parameters, the perspectives on food processing and engineering, and the nutritional aspects and culinary practices are considered. This review article discusses the current progress of additive manufacturing of food, the perspectives related to the food industry, and rheology as a tool where nonlinear behaviors are also included to unlock the analysis requirements for specific food groups for broader applications. The rheological methods for the characterization of food inks used in additive manufacturing technologies are critically reviewed, and key parameters are proposed.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".