Identification of Ubiquinones in Honey: A New View on Their Potential Contribution to Honey’s Antioxidant State
Bibliographic record
Abstract
Honey is composed of macromolecules arranged into multicomponent colloidal particles dispersed in a supersaturated sugar solution. The core part of colloidal particles in honey is made up of high-molecular weight protein-polyphenol complexes. We designed a multi-step extraction process to gain better insight into the phenolic compounds strongly bound to proteins in honey. Honeys were sequentially extracted by solvents of reduced polarities and the extraction process was monitored by LC-ESI-MS/MS. Unexpectedly, the results revealed ubiquinone-like compounds that partitioned to both, soluble supernatants and protein-bound insoluble residues from which they were released after the pronase-digestion of proteins. The accurate mass measurement and MS/MS fragmentation patterns using UPHLC-MS/MS coupled to quadrupole orbitrap confirmed their identification as ubiquinones. Distribution of ubiquinone-bound proteins was further investigated by the fractionation of honey protein-polyphenol complexes by size-exclusion chromatography followed by LC-ESI-MS analysis. Mass spectra revealed the presence of ubiquinones (UQs) in fractions of high polyphenol to protein ratio. The dominant mass peaks observed in these fractions were identified as UQ-3, UQ-5, and UQ-7. Since the quinone group of UQs is involved in redox reaction, we discuss the possibility that UQs may contribute to the antioxidant/proxidant activity of these complexes.
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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 | 0.001 |
| 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".