Synergies between plant antioxidant blends in preventing peroxidation reactions in model and food oil systems
Bibliographic record
Abstract
Abstract A study was conducted to investigate the oxidative behavior of various mixtures of rosemary, sage, and citric acid in a linoleic acid model system by oxygen consumption measurement and in a palm olein system by differential scanning calorimetry (DSC) analysis. Response surface methodology was used to optimize the use of the mixtures. Results showed that rosemary and sage were two important factors for the protective index (PI). The two antioxidants were highly significantly (P<0.001) in influencing PI values. There was a significant (P<0.01) synergistic effect between rosemary and sage on PI values. Citric acid was also found to be significant (P<0.05) for PI. With respect to onset time (To), all three antioxidants were significant (P<0.05). However, no significant interaction among antioxidants was found for To. Mathematical models for both PI and To could be developed with confidence. The R2 values for PI and To were 0.992 and 0.926, respectively. A combination of 0.078% rosemary, 0.067% sage and 0.037% citric acid was the optimal combination for PI, whereas a combination of 0.068% rosemary, 0.075% sage, and 0.039% citric acid was required to reach the optimal To value.
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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) | 0.001 | 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".