Optimization of Gluten-Free Bread Formulation Using Whole Sorghum-Based Flour by Response Surface Methodology
Bibliographic record
Abstract
The growing awareness of celiac disease and gluten sensitivities has generated interest in gluten-free products. Whole sorghum (Sorghum bicolor) is an excellent source of nutrients and is gluten-free. However, the absence of gluten makes it technologically challenging to produce leavened products. This research aims to utilize a response surface methodology to optimize the specific loaf volume and crumb firmness of a whole sorghum-based gluten-free bread formulation, evaluating different levels of milk powder, egg white, yeast, sugar, psyllium husk powder, xanthan gum, and soy lecithin. The models fit achieved an R2≥80%. The optimized formulation increased the specific loaf volume from 1.7 to 2.8 cm3 g−1 and decreased crumb firmness from 10.6 to 3.7 N compared to the initial gluten-free bread formulation (C1). Egg white, milk powder, and psyllium contribute to the formation of a gluten-like network, which enables gas retention, dough expansion, and volume increase. In addition, soy lecithin, among hydrocolloids, enhances dough stability and moisture retention, resulting in a softer crumb. Sensory evaluation indicated good consumer acceptability (average score of 7 on a 9-point hedonic scale), particularly for texture and flavor. These findings suggest that optimal formulation of sorghum achieves both technological and sensory properties, supporting its potential as a viable gluten-free bread alternative.
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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 | 0.001 | 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".