Evaluation of antioxidant potential of honey drops and honey lozenges
Bibliographic record
Abstract
The antioxidant potential of honey comes from various components including phenolic acids, enzymes (catalase, glucose oxidase), flavonoids, vitamins, organic acids, and Maillard reaction products that are nascently present in raw honey. Hence, it would be highly beneficial for consumers to develop honey-based candies such as honey drops and lozenges that can maintain health benefits of its precursors. This study is focused on evaluating the changes in chemical profiles of honey, specifically phenolic content and hydroxymethylfurfural (HMF) formation upon formulation of raw honey into honey lozenges and honey drops and evaluation of antioxidant properties of honey-based products using different antioxidant assays. Our results indicate that in comparison with raw honey, honey lozenges and drops contain higher total phenolic and HMF components, as well as exhibit greater antioxidant properties as is determined by multiple antioxidant assays.
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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".