3D printing of antioxidant-enriched plant-based meat analogue for the elderly: the role of wheat oligopeptide and grape seed extract
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the environmental hazards associated with excessive meat production and the impact of excessive consumption of red meat on human health, attention is being drawn to plant-based alternatives. Therefore, plant-based meat analogue are under widely research. However, there is still a lack of research focused on developing solely plant-based meat with enhanced nutritional benefits. This study aims to develop a functional plant-based meat ink utilizing 3D printing technology, which is expected to enhance the nutrition degree and customer attraction of plant-based meat. Soy protein isolate, xanthan gum, wheat oligopeptides, and grape seed extract were selected as raw materials. The results showed that the ink formulated with wheat oligopeptides and grape seed extract exhibited more suitable viscosity, stronger gel strength, and improved overall flavor. By comparing the texture properties with real meat inks, a combination of pea protein (7 g) or soy protein isolate (7 g), xanthan gum (0.25 g), wheat oligopeptides (1 g), and grape seed extract (0.5 g) was identified as the optimal ink formulation for plant-based meat analogue with potential antioxidant function for the elderly.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it