Evaluation of Gluten-Free Amaranth and Navy Bean Flour Blends on Quality of Sugar Cookies
Bibliographic record
Abstract
Gluten-free sugar cookies were made from amaranth (Amaranthus caudatus) and navy bean flours of different ratios. The physical properties of flour blends, dough, and cookies were evaluated. This study found that navy bean and its blends with amaranth had greater water holding capacity (WHC) than that of wheat flour. The increased WHC was observed as the amount of navy bean flours increased in blends. The amaranth flour had the highest water soluble index (WSI) and pasting viscosities. The WSI, pasting viscosities and rheological elastic properties of composites were improved by amaranth flour. Differences were also found in geometrical and textural properties of the doughs and cookies. The cookies made from flour blends have lower width and spread factors; however, they had higher thickness and volume resulting in higher yield compared to wheat flour. Overall, the cookies made by amaranth, navy bean, and their blends were rated “acceptable” in color, flavor, texture, and overall acceptability in the sensory study. The flavorings of vanilla, cinnamon, and almond extracts improved sensory scores of cookies made from blends, making them indistinguishable from cookies using wheat flour. This study suggested that the amaranth-navy bean blends could be good gluten-free candidates for health-promoting food products.
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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".