A comparison of blueberry polyphenols bioaccessibility in whey and pea proteins complexes and the impact of protein conformational changes on it
Bibliographic record
Abstract
Summary This study investigated the effects of pea and whey proteins on the bio‐accessibility and antioxidant activity of blueberry polyphenolic compounds during digestion using an in vitro gastrointestinal model (TIM‐1). The research objective was understanding how different protein sources affect blueberry polyphenols' bio‐accessibility and antioxidant activity. The study found that pea proteins resulted in higher bio‐accessibility and anthocyanin content compared to whey proteins. Additionally, interactions between blueberry polyphenols and proteins decreased the antioxidant activity. However, forming a non‐covalent and reversible complex between pea proteins and blueberry polyphenols may improve polyphenol stability and bio‐accessibility during digestion. The novel aspects of this research provide valuable insights into the effects of protein sources on the bio‐accessibility and antioxidant activity of blueberry polyphenols and may have important implications for developing functional foods and dietary supplements. The most important impact of the research is that pea proteins resulted in higher bio‐accessibility and anthocyanin content compared to whey proteins, which suggests that pea proteins may be a better protein source for combining with blueberries to maximise their health benefits. Additionally, forming a non‐covalent and reversible complex between pea proteins and blueberry polyphenols may improve polyphenol stability and bio‐accessibility during digestion, which has implications for the food industry in developing new products.
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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) | 0.001 | 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".