Quality changes in mixtures of hydrogenated and non‐hydrogenated oils during frying
Bibliographic record
Abstract
Abstract BACKGROUND: There is a need for frying oils with reduced trans fatty acid content and increased oxidative stability. This study was conducted to measure and compare frying oil quality parameters, namely colour, viscosity, free fatty acid (FFA) content and dielectric property, and to investigate changes in properties of mixtures of fully hydrogenated and non‐hydrogenated canola oils during deep‐fat frying of chicken nuggets. Proportions of hydrogenated oil to non‐hydrogenated oil used in the study were 0, 20, 40, 60, 80 and 100%. Chicken nuggets were fried at 190 °C. The relationships among quality parameters, frying time and oil type were investigated. RESULTS: Frying time and oil type had significant effects on all properties. First‐order kinetic equations were used to represent changes in colour, viscosity and FFA content. The rates of change of viscosity and FFA content were higher in non‐hydrogenated oil, whereas the rates of change of colour were similar in the different oils. Increasing the level of hydrogenated oil in the mixture resulted in only slight changes in quality parameters. CONCLUSION: Quality parameters, namely colour, viscosity, FFA content and dielectric property, of frying oil are significantly affected by frying time and hydrogenation level. Frying oil quality and stability can be adjusted by mixing hydrogenated and non‐hydrogenated oils. Copyright © 2008 Society of Chemical 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".