Examining the role of polyphenols in the in vitro starch hydrolysis of gluten free enriched flatbreads
Bibliographic record
Abstract
Polyphenols have been often related with reducing starch digestibility. Gluten free breads are rich in starch, their enrichment in phenolic acids can modulate starch digestibility. Aim is to evaluate the effect of three powdered ingredients (artichoke, tomato, lemon) at varying concentrations (2, 5, and 10 g/100 g) on the technological and in vitro digestibility of gluten free flatbreads (FB). The polyphenols profile content of the FB was quantified, and they were correlated with in vitro starch digestion parameters. The incorporation of artichoke, tomato, lemon powders in FB reduced luminosity and hardness. Specific phenolic compounds were identified in artichoke FB (apigenin-7-O-glucoside, syringic acid, catechin gallate, luteolin-7-O-glucoside, and luteolin-7-O-rutinoside), and in lemon FB (eriocitrin and hesperidin). The principal component analysis indicated that lemon-enriched FB exhibited the lowest starch hydrolysis, potentially due to its acidic pH and polyphenols content. These functional ingredients provide a promising alternative to obtain innovative gluten free bakery products. • Tomato, artichoke or lemon powders were added in gluten free flatbread up to 10 g/100 g. • Flatbread digestibility was influenced by polyphenol-rich ingredients and pH. • Lemon and artichoke powders inhibited starch hydrolysis in flatbreads. • Free and bound polyphenols of lemon and artichoke were related to starch hydrolysis.
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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) | 0.002 | 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".