Quinoa and pea protein used as a novel source for producing dysphagia-oriented food by 3D printing technique
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
Dysphagia, a prevalent condition affecting over 30% of the elderly, significantly elevates malnutrition risks due to impaired swallowing and insufficient nutrient intake. This study aimed to develop plant-based, 3D-printed dysphagia diets using pea protein isolate (PPI) combined with quinoa to enhance essential amino acid profiles, complemented by hydrocolloid —xanthan gum (XG), carboxymethyl cellulose (CMC), and agar—for tailored texture modulation. Eight ink formulations were evaluated based on molecular interactions, rheological behavior, 3D printing performance, and compliance with International Dysphagia Diet Standardization Initiative (IDDSI) standards. Synergistic effects of XG and CMC in Ink-C optimized shear-thinning properties and structural stability, enabling high-precision printing of self-supporting constructs. IDDSI testing confirmed that Ink-A and Ink-C met Level 5 “minced and moist” criteria, validated by texture parameters and shape retention during mechanical testing. Electronic nose showed minimal deviations in aromatic characteristics across all formulations, preserving sensory acceptability. In vitro digestion models revealed that hydrocolloid networks temporarily hindered gastric proteolysis but ultimately achieved sufficient intestinal hydrolysis (>76%) to ensure nutrient bioavailability. Ink-C was identified as the optimal formulation, harmonizing printability, swallow-safe textures, and digestibility. This work highlights the potential of hydrocolloid-engineered 3D printing to advance personalized nutrition for dysphagia management, offering scalable solutions to improve dietary diversity and clinical outcomes in aging populations.
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 | 0.001 |
| 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 it