Defects in 3D/4D food printing and their possible solutions: A comprehensive review
Bibliographic record
Abstract
3D food printing has recently attracted significant attention, both from academic and industrial researchers, due to its ability to manufacture customized products in such terms as size, shape, texture, color, and nutrition to meet demands of individual consumers. 4D printing, which is a technique that allows evolution of various characteristics/properties of 3D printed objects over time through external stimulation, has also been gaining more attention. In order to produce defect-free printed objects via both 3D and 4D printing, it is necessary to first identify the causes of defects and then their mitigation strategies. Comprehensive review on these important issues is nevertheless missing. The purpose of this review is to investigate causes and characteristics of defects occurring during and/or after 3D food printing, with a focus on how different factors affect the printing accuracy. Various techniques that can potentially minimize or eliminate printing defects and produce high-quality 3D/4D printed food products without the need for time-consuming trial and error printing experiments are critically discussed. Guidelines to avoid defects to improve the efficiency of future 3D/4D printed food production are given.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".