Optimization of synthetic antioxidant combinations to enhance oxidative stability and nutrient preservation in edible oils
Bibliographic record
Abstract
The oxidation stability of both vegetable oils and animal fats is crucial for maintaining their nutritional quality and safety. This study determined the optimal synthetic antioxidant combinations to enhance oxidative stability and preserve nutrients in various edible oils. Schaal method was used to simulate accelerated oxidation. Antioxidants (PG, TBHQ, VE, BHT) were added to vegetable oils (palm oil and soybean oil) and animal fats (lard and tallow) alone or in combination with PG and other antioxidants. Oxidation was assessed via acid value (AV), peroxide value (POV), anisaldehyde value, and DPPH free radical scavenging activity. Changes in fatty acid and nutritional composition (tocopherol), as well as the formation of harmful substances like benzo[a]pyrene, were analyzed. Principal component analysis identified the most effective antioxidant combinations for each oil. Results showed that PG and TBHQ together significantly boosted antioxidant properties. In palm oil, this combination reduced AV by 57.1 % after 15 days versus the control (1.12 mg/g), lowered POV to 0.08 g/100g, below the control's 0.19 g/100g, and increased DPPH scavenging to 57.5 μmol TE/kg. At the same time, PG and TBHQ effectively retained oleic acid (47.7 %) and α-tocopherol (10.41 mg / kg) in palm oil. In lard, PG-TBHQ lowered AV to 1.90 mg/g from the control's 2.64 mg/g, and enhanced DPPH scavenging to 59.1 μmol TE/kg, 44.1 % oleic acid was retained. This study demonstrates that PG and TBHQ combined improve edible oil's oxidative stability, offering a simplified, efficient antioxidant strategy for industrial oil production, ensuring quality, safety, and extended shelf life. • PG-TBHQ in oils enhances stability and retains nutrients more effectively than single antioxidants. • Study combines Schaal tests, DPPH scavenging, PCA, and tocopherol analysis for antioxidant evaluation. • PG-TBHQ reduces harmful byproducts and preserves nutrition, optimizing edible oil production.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".