Polyphenol-polysaccharide interactions: molecular mechanisms and potential applications in food systems – a comprehensive review
Bibliographic record
Abstract
Polyphenols, a major class of plant secondary metabolites, are well known for their diverse bioactive properties. It has also been established that polyphenols interact with other macromolecules, such as proteins, polysaccharides, and lipids in the food matrix. Among the primary metabolites of the plant, carbohydrates play a significant role. Polyphenols and polysaccharides form complexes upon interaction; this interaction could be through covalent or non-covalent bonds, such as electrostatic, hydrophobic, van der Waals forces, and hydrogen bonding. These polysaccharide-polyphenol complexes exhibit enhanced bioactivity and influence the digestibility of complex macronutrients (such as proteins and polysaccharides), as well as their biological efficacy, bioavailability, and stability. Despite their numerous benefits and potential applications, the underlying mechanisms of interaction and complex formation between polysaccharides and polyphenols, as well as the influence of their structural parameters, remain underexplored. This comprehensive review summarizes the basic molecular-level implications of polysaccharides and polyphenols, exploring their potential applications in the food industry, and provides a basic understanding of their occurrence in various food matrices, characteristics of both polyphenols and polysaccharides that influence their interaction mechanisms, and detection under laboratory conditions. The review aims to bridge the gap between the molecular-level understanding of the complex and the development of potential nutraceuticals and functional food ingredients based on polysaccharide-polyphenol complexation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".