Germinated/fermented legume flours as functional ingredients in wheat‐based bread: A review
Bibliographic record
Abstract
Refined wheat breads are consumed throughout the world as an energy-dense staple food. The consumption of refined wheat bread has raised concerns among health-conscious consumers. This has partly stimulated research interest in the inclusion of functional ingredients such as germinated/fermented legume flour in the development of nutritious and healthy breads to drive innovations in the bakery industry and overcome sustainability problems. Nevertheless, the inclusion of germinated/fermented legume flours cannot be a direct replacement of refined wheat, because processing requirements must be met. This critical review analyzes the impact of germinated/fermented legume flour on the rheological characteristics, nutritional quality, health-promoting, and technological properties of wheat-based bread for improved nutrition and health, identifying current challenges. The macroconstituent changes and the increasing enzyme activity produced during germination/fermentation influence the functionality of wheat dough and the resultant bread quality. Substitution of up to 20% germinated legume flour caused detrimental effects on technological properties of the bread, whereas better technological properties were recorded with up to 20% fermented legume flour. Nevertheless, more studies are needed to provide detailed insight on this observation. Germinated/fermented legume flour could serve as a functional ingredient for the development of nutritious and healthy breads. In fact, breads containing germinated/legume flour are rich in quality protein, dietary fiber, micronutrients, phytochemicals, and bioactive constituents and low in glycemic index with improved sensory properties compared to 100% wheat bread. Nonetheless, information on the bioavailability of nutrients in breads containing germinated/fermented legumes using in vivo studies and profiling the metabolites therein are scarce in the literature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".