Sensory and Textural Evaluation of Gluten-Free Bread Substituted With Amaranth and Montina™ Flour
Bibliographic record
Abstract
The objective of this study was to develop a nutrient-dense gluten-free bread (GFB) using either amaranth or Montina™ flour in a standardized gluten-free lean breadrecipe for the purpose of comparing the nutritional, sensory, and objective qualities of the developed breads to a commercially-marketed GFB. Participants (n=222)included individuals who typically eat a gluten-free diet and those who eat a non-restricted diet. The non-restricted diet group was used to assess product acceptability in the general population and to determine product marketability among those without gluten restrictions. Nutritionally, both developed breads provided at least 26% more iron than the commercial GFB and > 40% more fiber while the amaranth bread provided twice as much folate. Significant differences (p < 0.05) in sensory attributes (appearance, texture, flavor, tenderness, and overall acceptability) of both amaranth- and Montina™-based breads were not reported between the groups. Based on sensory scores using a 9-point Hedonic scale, the commercial GFB was preferred over either developed bread and the Montina™-based bread was preferred over the amaranth-based bread. Significant differences in bread hardness were not detected among the tested GFB, yet commercial GFB slices exhibited the largest and most consistent cell size throughout. Results suggest that amaranth and Montina™ flours assist in improving the nutritional quality of GFB, yet additional testing is needed to assist in formulation modifications of this standardized lean bread recipe in order to produce a product similar in sensory qualities to commercially-marketed GFB.
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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".