Quality attributes of bread fortified with staghorn sumac extract
Bibliographic record
Abstract
Staghorn sumac (Rhus typhina) is rich in polyphenols and may be used as an innovative ingredient in maintaining and enhancing food quality. In this report, aqueous extracts of sumac fruit powder were added up to 10% in wheat bread formulation. The extract concentration-dependently delayed the mold growth (up to 5 log reduction in 7-day storage) and the staling of bread. Adding sumac extracts dose-dependently increased the total phenolic and anthocyanin contents of the breads. Minimal changes were observed in loaf volume, water activity, moisture content, texture (cohesiveness, springiness, and adhesive), and aroma of breads containing extracts of less than 4%. Overall, sumac addition altered several quality attributes of bread, including hardness, color, and sensory acceptance in appearance, flavor, and texture. Sumac holds potential as a natural preservative and an antistaling agent in bread formulation. PRACTICAL APPLICATIONS: The rising healthy food market demands bakery products fortified with novel functional ingredients. Staghorn sumac is an emerging healthy food ingredient that has attracted much research attention recently. This study provides a scientific basis to develop bread fortified with sumac-derived functional ingredients. The results pointed out the feasibility of making sumac-fortified breads with enhanced phenolic content, increased shelf life, and acceptable sensory profile.
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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".