Antioxidative properties of phenolic acids and interaction with endogenous minor components during frying
Bibliographic record
Abstract
Abstract The ability of selected phenolic acids to improve the frying performance of canola oil was evaluated in a frying test. The frying performance of the oil was assessed by analysis of total polar components (TPC), level of 4‐hydroxynonenal (HNE), and the rate of formation of volatile carbonyl compounds (VCC). All the tested phenolic acids; ferulic acid (FA), caffeic acid (CA), dihydrocaffeic acid (HCA), gallic acid (GA), and vanillic acid (VA) significantly increased the frying performance of canola oil triacylglycerols (CTG). At the end of the frying test, the amount of TPC in CTG was 22.9 ± 1.0% compared to a maximum of 18.8 ± 0.8% in CTG fortified with the phenolic acids. Similarly, the level of HNE was reduced by up to 45% when it was supplemented with phenolic acids. The results showed that ethyl ferulate (EF) was a better antioxidant than FA under frying conditions; HCA offered a slightly better protection than CA; and the cinnamic acid derivative, FA was better than VA, its benzoic acid analogue. A significant synergy was observed between phenolic acids and the sterol fraction isolated from canola oil. The observed synergy was attributed to the possible formation of steryl phenolates during the frying test. Practical applications: The poor thermal stability of polyunsaturated oils limits their application for prolonged frying. PUFA offer important health benefits and can improve nutritional value of fried foods. Contrary to the commonly applied synthetic antioxidants, the phenolic acids tested in this study often are part of endogenous oil components present in oilseeds and also in some oils, and are known for their positive health benefits. Thus, the simple phenolic acids, especially the cinnamic acid derivatives may be applied as potent antioxidants to protect oils during thermal processes used for food production.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".