Effect of hydrophilic colloids containing resveratrol on dough processing and bread quality
Bibliographic record
Abstract
Resveratrol (RSV) is a bioactive polyphenol with proven health benefits including anti-diabetic effects, but its poor stability has limited food-based applications. This study aimed to examine the impact of the breadmaking constraints on RSV and to enhance RSV retention and functional efficacy in bread by protecting RSV with cost-effective gelling materials such as corn starch, pea starch, xanthan gum and locust bean gum. Free RSV or RSV-enriched gels were incorporated into flour (0.5% w/w) and mixed to check the RSV recovery. Furthermore, in vitro starch hydrolysis kinetics were assessed. Our results showed that mixing was the breadmaking stage with greatest impact on RSV recovery, and it was independent of the mixing speed. Mixolab analysis showed that RSV-enriched gels counteracted the dough stability reduction induced by free RSV. Corn starch and xanthan gum gels significantly improved RSV recovery from dough (44.01% and 39.04%, respectively), compared to free RSV containing dough (34.97%). Slower starch hydrolysis was observed when RSV was added in the dough. Importantly, RSV-enriched corn starch gels led to breads with reduced hardness (1086 g vs 2030 g) and higher cohesiveness (0.84 vs 0.72) compared to breads containing free RSV. This work highlights the potential of starch-hydrocolloid gels to stabilize RSV in bakery products and facilitate its incorporation into functional foods for metabolic health. • RSV loss mainly occurred during mixing with minimal degradation during baking. • Corn starch gels enhanced RSV recovery and recovery during mixing. • RSV-enriched corn starch gels reduced in vitro starch hydrolysis. • Corn starch gels improved RSV recovery and bread texture.
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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 | 0.001 |
| 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".