Deterioration Kinetics of Crude Palm Oil, Canola Oil and Blend During Repeated Deep‐Fat Frying
Bibliographic record
Abstract
Abstract The oxidation of vegetable oils is generally treated as an apparent first order kinetic reaction. This study investigated the deterioration of crude palm oil (CPO), refined canola oil (RCO) and their blend (CPO:RCO 1:1 w/w) during 20 h of successive deep‐fat frying at 170, 180 and 190 °C. Kinetics of changes in oil quality indices, namely, free fatty acid (FFA), peroxide value (PV), anisidine value (p‐AV), total polar compounds (TPC) and color index (CI) were monitored. The results showed that FFA and PV accumulation followed the kinetic first order model, while p‐AV, TPC and CI followed the kinetic zero order model. The concentration and deterioration rate constants k, increased with increasing temperatures. This effect of temperature was modeled by the Arrhenius equation. The results showed that PV had the least activation energies Ea (kJ/mol) values of 5.4 ± 1 (RCO), 6.6 ± 0.7 (CPO) and 11.4 ± 1 (blend). The highest Ea requirement was exhibited by FFA with a range of 31.7 ± 3–76.5 ± 7 kJ/mol for the three oils. The overall Ea values showed that the stability of the blend was superior and not just intermediate of CPO and RCO. The correlation of the other oil quality indices with TPC indicated a positive linear correlation. The p‐AV displayed the strongest correlation, with mean correlation coefficient rs of 0.998 ± 0.00, 0.994 ± 0.00 and 0.999 ± 0.00 for CPO, RCO and blend, respectively.
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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) | 0.000 | 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) | 0.000 | 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".