Functional Model Beverages of Saffron Floral By-Products: Polyphenolic Composition, Inhibition of Digestive Enzymes, and Rheological Characterization
Why this work is in the frame
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Bibliographic record
Abstract
Despite the rapid and dynamic evolution of research into dietary polyphenols, there is still a knowledge gap regarding their bioaccessibility since it could be influenced by the chemical and nutritional compositions of the food matrix. This study aimed to describe the impact of food thickeners (xanthan gum, guar gum, β-glucan, pectin) on the bioactivity of flavonoids from saffron floral by-products in model beverages before and after thermal processing. The different beverage formulas were characterized in terms of polyphenolic composition using HPLC-DAD-ESI-MSn and rheological properties. The impact of food thickeners and thermal processing on the inhibition of digestive enzymes was also determined. The model beverages mainly presented glycosylated flavonols (of kaempferol, quercetin, and isorhamnetin), with a reduced content in some heat-treated samples. The inhibitory effect on α-amylase was only detected in heat-treated beverages, showing the formulation without any thickener to have the greatest inhibitory effect. Finally, the presence of saffron floral by-products in the beverages showed a tendency to decrease the flow consistency index (K) and an increase in the flow behavior index (n), most probably driven by the aggregation of phenolics with thickeners. Therefore, this research provides new insights into the development of flavonoid-rich beverages in order to ensure that they exert the expected beneficial effects after their ingestion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it