Synergistic, Additive, and Antagonistic Effects of Food Mixtures on Total Antioxidant Capacities
Bibliographic record
Abstract
Different foods possess different bioactive compounds with varied antioxidant capacities. When foods are consumed together, the total antioxidant capacity of food mixtures may be modified via synergistic, additive, or antagonistic interactions among these components, which may in turn alter their physiological impacts. The main objective of this study was to investigate these interactions and identify any synergistic combinations. Eleven foods from three categories, including fruits (raspberry, blackberry, and apple), vegetables (broccoli, tomato, mushroom, and purple cauliflower), and legumes (soybean, adzuki bean, red kidney bean, and black bean) were combined in pairs. Four assays (total phenolic content, ferric reducing antioxidant power, 2,2-diphenyl-1-picrylhydrazyl, radical scavenging capacity, and oxygen radical absorbance capacity) were used to evaluate the antioxidant capacities of individual foods and their combinations. The results indicated that within the same food category, 13, 68, and 21% of the combinations produced synergistic, additive, and antagonistic interactions, respectively, while the combinations produced 21, 54, and 25% synergistic, additive, and antagonistic effects, respectively, across food categories. Combining specific foods across categories (e.g., fruit and legume) was more likely to result in synergistic antioxidant capacity than combinations within a food group. Combining raspberry and adzuki bean extracts demonstrated synergistic interactions in all four chemical-based assays. Compositional changes did not seem to have occurred in the mixture. Results in this study suggest the importance of strategically selecting foods or diets to maximum synergisms as well as to minimum antagonisms in antioxidant 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".