Deep-fat Frying of Vegetable Oils: Major Chemical Reactions and Effect of Natural Extracts on Oxidative Stability - A Review
Bibliographic record
Abstract
Deep-fat frying is a popular cooking technique in the world and vegetable oil is widely used as the frying medium in this process. Deep-fat frying produces both desirable and undesirable compounds through various chemical reactions. Undesirable chemical compounds are formed mainly through hydrolysis, oxidation and polymerization reactions. These compounds lower the oxidative stability thereby, the quality of oil and food. Antioxidants are added to improve the oxidative stability of oil during deep-fat frying by lowering the free radical action in frying oil. Even though artificial antioxidants are added to frying oils to lower the effects of undesirable chemical reactions and their products, the efficiency of artificial antioxidants decrease with increasing temperature and may cause adverse health effects to the consumer. Natural extracts of rosemary (Rosmarinus officinalis), sage (Salvinia officinalis), tea (Camelia sinensis), oregano (Origanum vulgare) and barley (Hordeum vulgare) are stable under frying conditions and act as effective antioxidants during deep-fat frying. Despite confirmation by numerous research, the use of natural compounds as antioxidants in deep-fat frying is not popular in the food industry. Hence this review explores the major chemical reactions in vegetable oils during deep-fat frying and the effect of natural compounds and extracts in interrupting these undesirable chemical reactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".