Physicochemical, Rheological and Sensory Evaluation of Herbal Bread Containing Turmeric, Ginger, and Black Cumin Powder
Bibliographic record
Abstract
The diversity in the global food market is expanding as thousands of new products enter the business every year, among which nutraceutical and functional foods hold important positions. The present research work aimed at the nutritional evaluation of three medicinal herbs, i.e., turmeric (Curcuma longa L.), ginger (Zingiber officinale), and black cumin (Nigella sativa). A bread formulation was enriched with the individual/combined supplementation (1–3%) of these herbs. Later, the bread was analyzed for nutritional, rheological, textural, and sensorial characteristics. The results revealed that the herbs improved the nutritional composition of bread, especially ash and fiber, as the maximum ash and fiber contents were noticed in T15 (2.0% dried powder of each plant) with values of 1.64 ± 0.04% and 4.63 ± 0.16%, respectively. The results regarding the rheological behavior showed minor variations in the rheological traits and a slight increase in dough development time up to 4.50 ± 0.20 min in T10 from 2.80 ± 0.13 min in T0. The sensorial attributes also indicated their marked suitability as external and internal characteristics were least affected by the addition of the herbs. Although some parameters like the crust and crumb colors were affected by the addition of black cumin, showing values of 6.25 ± 0.52 and 4.44 ± 0.19, respectively, in T15, and aroma characteristics were affected by the addition of ginger, supplementation with a combination of herbs at lower doses mitigated the adverse effects of other herbs. Moreover, shelf-life extension, especially with the addition of turmeric powder, was the hallmark of this research. This study concluded that medicinal herbs can be incorporated into baked products to improve the nutritional and sensorial attributes of functional herbal bread.
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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 | 0.001 |
| 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".