Lupin flour as a wheat substitute in conventional and sourdough breadmaking: impact on bread physicochemical properties and volatile profile
Bibliographic record
Abstract
Abstract Enhancing the nutritional profile of baked goods while addressing sustainability challenges means finding different sources of functional, sensory and nutritional ingredients. The aim of this study was to evaluate native lupin flour versus spontaneously fermented lupin flour as ingredient for wheat breadmaking. For that purpose, wheat flour was supplemented with 15–30 g/100 g lupin flour (LF15, LF30) or freeze-dried lupin sourdough (LS15, LS30) and dough and breads were assessed in comparison with wheat bread (control). Both lupin flour and lupin sourdough decreased dough stability, delayed the fermentation and lowered the pH. The incorporation of lupin flour increased the hardness of the crumb, except for when adding sourdough (15 g/100 g) that increased the bread expansion and enriched the volatile profile of bread. The analysis of the volatile compounds confirmed that lupin flour conferred fatty, green odor due to octanal, and when in the form of sourdough brought sour, and almond notes from acetic acid and benzaldehyde, respectively. Overall, lupin addition is a strategy to produce bread aligned with current trends towards sustainable and plant-based diets, particularly in the form of spontaneous type IV whole lupin sourdough up to 15 g/100 g wheat replacement.
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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 | 0.001 | 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".