Enhancing 3D Printing Performance and Product Quality Through the Valorization of Food By‐Products and Waste
Bibliographic record
Abstract
This review systematically explores the emerging use of food by-stream materials in 3D printing (3DP) applications, addressing the pressing need for sustainable resource utilization in the food sector. The review evaluates the potential of food by-products and waste, ranging from agricultural, animal, marine, microbial fermented biomass, and filamentous-fungi by-products to food consumption waste, for integration into 3DP techniques like fused deposition modeling, paste extrusion, direct ink writing, and selective laser sintering. Utilizing the PRISMA 2020 framework, a comprehensive literature analysis identified 80 relevant studies, categorized by material type and application. The findings indicate that plant-based by-stream materials encompassing sources like vegetable residues, fruit peels, nut and bean shells, and grain husks, dominate current 3DP research. These materials support biocomposite advancements across various fields, with notable applications in food-safe packaging, biomedical scaffolds, nutritious snacks, and sustainable construction materials. Several studies highlight significant improvements in mechanical strength, such as tensile and compressive performance, alongside enhanced biodegradability of nonedible printed products and nutrient content in edible printed products. Key process parameters, including extrusion speed, nozzle temperature, and layer thickness, have been optimized to accommodate the unique properties of these food by-stream materials, ensuring printing fidelity, smooth extrusion, and structural integrity, thereby maximizing their potential across diverse 3DP techniques and applications. This review highlights 3DP as a transformative approach in resource recovery, demonstrating how incorporating food by-stream materials aligns with circular economy goals by reducing waste and enabling eco-friendly production. By advancing customizable, nutrient-dense, and sustainable products, 3DP of food by-stream materials holds significant promise for addressing global food security and sustainability challenges.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".