Exploring the functional potential of pea-based sourdough in traditional durum wheat focaccia: Role in enhancing bioactive compounds, in vitro antioxidant activity, in vitro digestibility and aroma
Bibliographic record
Abstract
Legume-based sourdough is gaining momentum. This study aimed to compare the effectiveness of type I sourdough prepared with durum wheat semolina (S1), pea flour (S2) or 50:50 semolina/pea flour (S3) in improving the nutritional quality, antioxidant compounds, in vitro digestibility and aroma of traditional durum wheat focaccia . Six focaccias were prepared: three with 40 % of S1, S2 and S3, and three with the corresponding amount of unfermented flours. Pea sourdough increased the content of phenolic compounds (8.82 ± 0.12 mg GAE/g d.m. in focaccia with 40 % pea flour and 4.92 ± 0.41 mg GAE/g d.m. in unfermented semolina focaccia ), and consequently increased the antioxidant activity. Focaccias with pea flour or pea sourdough were “source of protein” and “high fiber”, according to UE Reg. 1924/2006. Pea sourdough slowed down starch in vitro digestibility while enhancing protein digestibility and leading to a more complex volatile profile, with increased content of aldehydes, alcohols and Maillard reaction compounds. • Pea flour and pea sourdough partially replaced semolina in focaccia. • Antioxidant activity and phenolic compounds increased with pea-based sourdough. • Focaccia with pea flour was “source of protein” and “rich in fibre”. • Pea sourdough slowed down starch digestion and increased protein digestibility. • Pea flour and sourdough impacted on the volatile compounds of focaccia.
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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) | 0.000 | 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".