Acorn flour and sourdough: an innovative combination to improve gluten free bread characteristics
Bibliographic record
Abstract
Abstract Nowadays, challenges in gluten free breads (GFB) are focused on improving the nutritional and health benefits. Acorn flour is an underexploited sustainable ingredient, naturally gluten free, with many nutritional and technological advantages. The aim of this study was to explore the interaction of acorn flour supplementation (up to 35%) to rice flour and sourdough process to obtain rice based GFB. Different levels of rice flour replacement with acorn flour (0%, 23% and 35%), and sourdough (20%) were tested in a basic GFB recipe, and technological, nutritional, and functional GFB characteristics evaluated. The combination of acorn flour and sourdough was responsible for acidifying dough and bread. Breads containing 35:65 acorn flour: rice flour led to harder breads with lower crumb luminosity and with reddish and brownish tones, besides improved structural features when adding sourdough. That combination of sourdough and acorn flour reduced the rate and the extent of starch hydrolysis, as well as increase the minerals content, total phenolic compounds and antioxidant activity. Therefore, the combination of acorn flour and sourdough process allows obtaining rice based GFB with better nutritional pattern.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".