Effect of quinoa, chia and millet addition on consumer acceptability of gluten‐free bread
Bibliographic record
Abstract
Summary There is an increasing demand for gluten‐free foods; however, standard gluten‐free foods are deficient in nutrients. This study investigated the use of alternative grains (chia, millet and quinoa) in gluten‐free breads to evaluate their sensory properties (fresh and following a partial bake method). A sensory trial ( n = 98) asked participants to consider six fresh bread samples made from chia, millet and quinoa, using 9‐point hedonic scales and check‐all‐that‐apply. A second sensory trial ( n = 89) was then completed using par‐baked bread samples of the different formulations. The sensory properties and the acceptability of the bread were significantly affected by the chia and quinoa flour. The millet flour did not change the acceptability of the bread. Furthermore, the partial baking method (after 90 days of frozen storage) did not significantly affect the acceptability of the breads made with chia, millet and quinoa, but it did affect the acceptability of the control bread prepared with brown rice flour. Overall, millet flour could be incorporated into gluten‐free breads made following a partial baking method without affecting consumer acceptability. Future studies should use a trained panel to evaluate how the breads differ based on the partial baking method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".