Development of Gluten-Free Egg Pasta based on Amaranth, Maize and Sorghum
Bibliographic record
Abstract
Due to increase of the population affected by Coeliac disease and awareness of consumers about the relationship between food and health, the production of cereal products from raw materials other than wheat is of interest. The aim of the work was to produce good quality gluten-free spaghettis, based on sorghum, amaranth and maize flour. Response surface methodology was applied to determine optimal formulation for the production of pasta. The resulting products of the experimental design were characterized regarding humidity, color, density, water activity, texture (firmness and elasticity), optimal cooking time, cooking weight, and cooking loss. The main trials were compared to two industrials gluten-free spaghetti. The results showed that water activity and cooking weight of the main trials were similar to the industrials pasta, but were really different for the other studied parameters. The optimal formulation was determined in order to obtain pasta with low cooking loss and optimal color and texture firmness. It utilized 12% egg white, 60% maize flour, 30% sorghum flour, 10% amaranth flour, 2.4% guar gum and 36% water. The optimal formulated pasta was evaluated by an untrained consumer panel in a sensorial analysis to show the consumer acceptance. The product presents nutritional benefits, the color was appreciated, but the texture was granular and crumbly which was not liked by the consumers.
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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".