Performance of palm olein and modified rapeseed, sunflower, and soybean oils in intermittent deep‐frying
Bibliographic record
Abstract
The frying performance of rapeseed, soybean, and sunflower oils with modified fatty acid composition, and palm olein (PALMO) was compared during a rotational frying operation. The frying was conducted at 185 ± 5°C for 6 days where French fries, battered chicken, and fish sticks were fried in succession. At the end of the frying period, high‐oleic rapeseed and sunflower oils exhibited a significantly higher frying stability than PALMO and other modified oils, based on total polar components (TPC), polymers, and non‐volatile carbonyl compounds formation (anisidine value (AV)). The rate of TPC formation was 2.9, 2.9, 3.2, 3.2, and 3.4% per frying day for high‐oleic low‐linolenic rapeseed (HOLLRAP), high‐oleic sunflower (HOSUN), mid‐oleic sunflower (MOSUN), low‐linolenic soybean (LLSOY), and PALMO, respectively. Although the contents of free fatty acids (FFA) in the used oils were significantly below the regulatory discard level, in PALMO formation of these compounds was 1.7 times higher compared to the modified oils. Color component formation and tocopherol degradation were also observed to be the highest in palm olein. A 15‐member consumer panel awarded HOLLRAP and HOSUN the highest overall sensory acceptance scores, while for LLSOY and PALMO the lowest. Practical applications: Although several frying oils are available in today's market, only a few of them can deliver satisfactory performance during extended frying operation. Thus, the search for the ideal frying oils/fats is an ongoing task. The present study assessed frying performance in the quest for the appropriate frying oils/fats in order to deliver healthy fried products with optimized nutritional qualities.
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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".