Comparative Analysis of Frying Performance: Assessing Stability, Nutritional Value, and Safety of High-Oleic Rapeseed Oils
Bibliographic record
Abstract
Frying is a critical process in the food industry, where selecting appropriate vegetable oils is key to achieving optimal results. In this study, French fries were fried at 175 °C with five different oils, the changes in the physicochemical indexes and free radical scavenging rate of the oils during the frying process were investigated, and the most suitable oils for frying were identified through comparative analysis using principal component analysis (PCA). We assessed the frying performances of hot-pressed high-oleic-acid rapeseed oil (HHRO), cold-pressed high-oleic-acid rapeseed oil (CHRO), soybean oil, rice bran oil, and palm oil utilizing principal component analysis over an 18 h period. The HHRO and CHRO showed lower acid values (0.31, 0.26 mg/g), peroxide values (2.09, 1.96 g/100 g), p-anisidine values (152.48, 178.88 g/mL), and total polar compound percentages (27.60%, 32.10%) than other oils. Furthermore, both the HHRO and CHRO demonstrated enhanced free radical scavenging abilities, indicative of their higher antioxidant capacities, as corroborated by the PCA results. Benzopyridine, 3-monochloropropane-1,2-diol ester, squalene, tocopherols, and polyphenol from the HHRO and CHRO during frying were compared. A comprehensive examination of harmful substances versus nutrient retention during frying revealed that the HHRO contained fewer hazardous compounds, while CHRO retained more nutrients. Therefore, this study analyzes the oxidation regulation of HHRO in frying applications, highlights the prospects of HHRO for frying in terms of health and economy, and contributes valuable insights for informed vegetable oil selection within the food industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".