Combinatorial Interactions of Essential Oils Enriched with Individual Polyphenols, Polyphenol Mixes, and Plant Extracts: Multi-Antioxidant Systems
Bibliographic record
Abstract
With the aim to develop essential oil (EO) multi-antioxidant systems, combinatorial interactions of selected phenol and terpene-rich EOs (from Pimento Berry, Ceylon Cinnamon, Clove, Sage, White thyme; Oregano) enriched with individual polyphenols, crude plant extracts, and mixtures of their major polyphenols were investigated using single electron transfer (SET)-based DPPH and hydrogen atom transfer (HAT)-based ORAC assays. Polyphenols that enriched Eos the most favorably were rosmarinic acid (IC50 of 0.0891–0.1448 mg enriched EO/mg DPPH; 5772–17,879 µmol TE/g enriched EO) and quercetin (IC50 of 0.0682–0.1060 mg enriched EO/mg DPPH; Trolox Equivalents (TE) of 9776–14,567µmol /g enriched EO), whereas p-coumaric acid (IC50 of 0.1865–1.1424 mg enriched EO/mg DPPH; 7451.00–11,588 µmol TE/g enriched EO) and rutin hydrate (IC50 of 0.1140–0.3112 mg enriched EO/mg DPPH; 2298–6227 µmol TE/g enriched EO) were the least favorable. Enrichments with polyphenol mixes and crude extracts exhibited synergistic and additive effects in the SET-based DPPH assay. In the HAT-based ORAC assay, EO enrichments with crude extracts exhibited more additive effects, as well as less antagonistic effects, than enrichments with their major polyphenol mixes, revealing the significant contributions of minor compounds. EOs enriched with crude green tea and apple extracts exhibited synergistic or additive effects, whereas EOs enriched with grape seed and rosemary extracts exhibited equal antagonistic effects. Predictive models were developed to explain the variability between the observed and predicted antioxidant activities of enriched EOs.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".