Development and Testing of Gluten-Free Pasta Based on Rice, Quinoa and Amaranth Flours
Bibliographic record
Abstract
The goal of this study was to make high quality gluten-free pasta using amaranth, quinoa and rice flours, water and eggs using extrusion processing, and to compare these with gluten-free pasta already commercialized. The difficulty was to reproduce the texture provided by the gluten network without using gluten. To do that, an experimental design was created in order to make samples with different quantities of each grain, egg whites and water. Samples were manufactured and various tests (e.g., color analysis, water activity, cooking loss, texture, etc.) were carried out in order to find the best formulation, namely the formulation which was closest to Barilla or Andean dream gluten-free commercial pasta. With Rcommander software, results were analyzed and it was determined that the best pasta formulation was 10% amaranth flour, 40% quinoa flour, and 50% rice flour, with 18% eggs whites and 39% water. This optimal formulation was manufactured and subjected to sensory analysis with other commercial samples (Barilla, Andean Dream). Statistical analyses were conducted and it was shown that, even though this formulation did not quite achieve Barilla or Andean Dream pastas quality, it approached closely in some parameters. Indeed, 80% of consumers did not refuse to eat this pasta again, and with addition of tomato sauce, no differences were seen between the spaghettis. However, individual sample analysis did indicate that consumers did not appreciate the formulation’s sticky texture, thus this parameter would have to be reworked to achieve higher quality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".