Recent Advances in Plant-Based Edible Emulsion Gels for 3D-Printed Foods
Bibliographic record
Abstract
3D printing has emerged as a suitable technology for creating foodstuff with functional, sensorial, and nutritional attributes. There is growing interest in creating plant-based foods as alternatives to address current demands, especially to tailor consumer preferences. Consequently, plant-derived edible inks for additive manufacturing have emerged as suitable options, including emulsion gels (or emulgels). These gels can be formulated entirely from plant-derived lipids, proteins, polysaccharides, and/or other ingredients to form complex fluids that belong to the category of soft matter. This review summarizes the most recent advances in the areas of formation, structuring, properties, and applications of plant-based emulsion gels for 3D-printed food. These semisolid materials can be extruded to the set or solidified into structures with predesigned shapes, fidelity, and sensory attributes across the senses (taste, smell, sight, and touch) along with nutrition values. Emulsion gels can be formed by either solely gelling the continuous phase or combining this process with the formation of a particle network through aggregation and close packing. The current challenges facing the development of edible inks using plant-based materials are critically discussed to stimulate further advances in the rapidly growing field of personalized 3D-printed foods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".