Quinoa and pea protein used as a novel source for producing dysphagia-oriented food by 3D printing technique
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.
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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 | 0.000 | 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) | 0.001 | 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 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".