Printability and Thermophysical Properties of Three-Dimensional-Printed Food Based on “Cochayuyo” Durvillaea antarctica Seaweed Flour
Bibliographic record
Abstract
This research assessed the feasibility of adding Cochayuyo seaweed flour (at 30, 50, and 70% levels) to rice flour-based paste to improve its 3D printing quality. The paste’s rheological properties, printing quality, texture profile, thermal properties, and color of 3D-printed foods were explored. Results showed that pastes with Cochayuyo addition exhibited shear-thinning behavior, and viscosity increased with increased Cochayuyo concentration. Viscoelastic properties and a Texture Profile Analysis (TPA) revealed that Cochayuyo improved mechanical strength and made the paste easier to flow, improving printed food’s extrudability, fidelity, and shape retention, which was better observed in RC50 and RC70 printed at 15 mm s−1. A differential scanning calorimetry (DSC) analysis showed a partial substitution of rice flour for Cochayuyo flour in the formulation. This increased the onset and melting peak temperatures and reduced the enthalpy of fusion. CIE color parameters a*, b*, and L* showed that Cochayuyo addition increased the color to yellow and red; however, lightness was considerably reduced. Therefore, Cochayuyo flour could have the potential to be used for the manufacture improvement of 3D-printed food with better rheological, mechanical, thermal, printing quality, and nutritional properties, making possible the exploitation of the native Cochayuyo seaweed, which is highly available in Chile.
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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 | 0.000 | 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) | 0.000 | 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".