Colloidal structure of honey and its influence on antibacterial activity
Bibliographic record
Abstract
Abstract Honey colloidal structure emerges as a new trend in research on honey functions since it became recognized as a major factor altering bioactivity of honey compounds. In honey complex matrix, macromolecules self‐associate to colloidal particles at the critical concentration, driven by honey viscosity. Sequestration of macromolecules into colloids changes their activities and affects honey antibacterial function. This review fills the 80‐year‐old gap in research on honey colloidal structure. It summarizes past and current status of the research on honey colloids and describes physicochemical properties and the mechanisms of colloid formation and their dissociation upon honey dilution. The experimental observations are explained in the context of theoretical background of colloidal science. The functional changes and bioactivity of honey macromolecules bound to colloidal particles are illustrated here by the production of H 2 O 2 by glucose oxidase and the effect they have on antibacterial activity of honey. The changes in the production of H 2 O 2 and antibacterial activity of honey were coordinated with the changes in the aggregation–dissociation states of honey colloidal particles upon dilution. In all cases, these changes were nonlinear, assuming an inverted U‐shaped dose–response curve. At the curve maximum, the production of H 2 O 2 and antibacterial activity reached the peak. The curve maximum signaled the minimum honey concentration required for the phase separation. With phase transition from two‐phase colloidal condense state to dilute state dispersion, the change to opposite effects of dilution on these honey's activities occurred. Thus, the colloidal structure strongly influences bioactivity of honey compounds and affects its antibacterial activity.
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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".