Effects of repeated deep-frying on fatty acid profiles of potato fries and frying oils: soybean oil, canola oil and their 1:1 blend
Bibliographic record
Abstract
Introduction: This study looked at the fatty acid composition changes in potato fries fried in three different types of oils, namely soybean oil (SO), canola oil (CO), and a 1:1 blend of soybean oil and canola oil (SCO), throughout an intermittent frying process of 80 batches in five consecutive days. The study also examined the fatty acid composition changes in SO, CO and SCO during the frying process. Methods: Fat from potato fries (extracted by Soxtec system) and oil samples from the corresponding frying oil were analysed by gas chromatography-mass spectrometer (GC-MS) to examine the fatty acid profile changes during the deep-frying process. Results: Linoleic acid (LA) and a-linolenic acid (ALA) in all three oils decreased, while oleic acid (C18:1), stearic acid (C18:0), palmitic acid (C16:0) and octanoic acid (C8:0) increased. Formation of C18:1 trans fatty acid was observed as the frying time increased. The fatty acid composition of the potato fries was consistent with the fatty acid composition of the corresponding frying oils. Conclusion: Our results showed that blending soybean oil and canola oil did not significantly improve the frying stability of the resulting oil in terms of fatty acid profile. Due to the formation of trans fatty acids and the decrease in polyunsaturated fatty acids, our study also recommends not to use the same frying oil repeatedly and not to consume food products cooked in reused oil.
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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) | 0.001 | 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 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".