The effects of different levels of heat‐treated legume flour on nutritional, physical, textural, and sensory properties of gluten‐free muffins
Bibliographic record
Abstract
Abstract Background and objectives There is an increasing interest in the development of gluten‐free products for patients with celiac disease and nonceliac consumers. Three different ratios (80:20, 65:35, and 50:50) of legume flour to waxy rice flour and two legume species (mungbean and cowpea) were used to prepare gluten‐free muffins. Chemical composition, physical properties, texture, and sensory attributes of these muffins were evaluated. Findings Legume‐based muffins had lower specific volume but higher firmness than control muffin due to decreased number and area of air cells. Protein content in gluten‐free muffins was increased with increasing levels of legume flour. However, muffins with 80% legume flour had the lowest specific volume with increased gummy layers in crumb structure. Muffins containing 1:1 legume‐waxy rice flour mixtures showed the greatest physical properties, textural behavior, visual appearance, sensory score of texture, and overall acceptability comparable to control muffin. Conclusions These results indicate that the 1:1 legume‐waxy rice flour mixture might be suitable as an ingredient for gluten‐free bakery products with considerable potential. Significance and novelty This study suggests that the legume‐based muffins could be a promising alternative to wheat flour for making gluten‐free bakery products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".