Optimization of physicochemical changes of palm olein with phytochemical antioxidants during deep‐fat frying
Bibliographic record
Abstract
Abstract Response surface methodology (RSM) was used to optimize the amounts of rosemary and sage extracts together with citric acid as synergist antioxidants in stabilizing refined, bleached, and deodorized palm olein during repeated deep‐fat frying of potato chips. For all physicochemical properties studied, these phytochemical antioxidant treatments significantly (P<0.05) reduced the oxidation rate of the oil. During 5 d of frying, anisidine value, peroxide value, free fatty acid, polymer content, color units, viscosity, and absorbances at 232 and 268 nm gradually increased, whereas iodine value and ratio of 18∶2/16∶0 decreased. Further statistical analyses, including coefficient of determination (R2) and probability of F values, indicated that mathematical models for each physicochemical parameter could be developed confidently in this study, with R2 for all parameters greater than 0.90. These results suggested that an optimal mixture of phytochemical antioxidants derived from rosemary and sage together with citric acid could be produced using RSM for stabilizing thermally processed oil. For many physicochemical parameters examined, the use of moderate levels of antioxidants could result in optimal responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".