Polyphenol-polysaccharide interactions: molecular mechanisms and potential applications in food systems – a comprehensive review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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