Effect of semolina replacement with a raw:popped amaranth flour blend on cooking quality and texture of pasta
Bibliographic record
Abstract
The replacement of semolina (SEM) with raw:popped (90:10) amaranth flour blend (AFB) in pasta making at 25, 50, 75, and 100 g/100 g levels (flour basis, 14 g of water/100 g) was carried out to evaluate the effects on cooking quality and texture of the supplemented pasta samples. Significant differences on cooking quality characteristics and texture of the pasta samples were observed. The pasta solid loss increased, weight gain and firmness decreased as the AFB level increased. The semolina pasta showed the lowest solid loss (7 g/100 g) and the highest weight gain (188.3 g/100 g) and firmness (1.49 N), whereas the amaranth blend pasta was the softer (around half of the firmness of semolina pasta) and lost the higher amount of solids (11.5 g/100 g). The raw and popped AFB was suitable for increasing the nutritional quality through dietary fiber and high quality protein and even to obtain gluten-free pasta with acceptable cooking quality (solid loss of 3.5 g/100 g higher than that considered as acceptable for semolina pasta). The amaranth blend used in this study enables the partial or total replacement of wheat semolina in pastas with acceptable cooking quality and texture.
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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".