Tailoring oil blends for specific purposes: A study on nutritional and antioxidant properties of soybean oil mixed with corn, sunflower, and flaxseed oils
Bibliographic record
Abstract
Soybean oil (SO), recognized for its nutrients and affordability, often oxidizes, leading to nutrient degradation. This study presents a methodology for tailor oil blends to meet specific requirements, using SO blended with corn oil (CO), sunflower oil (SFO), and flaxseed oil (FSO). We analyzed the physicochemical properties, nutrient composition, and antioxidant capacity of various blends. Pearson's correlation analysis was utilized to examine the interrelation between nutrient composition and antioxidant capacity. The principal component analysis identified suitable blend ratios based on the desired nutrient composition and antioxidant capacity. Blend F3 (60% SO, 15% CO, 16% SFO, 9% FSO) demonstrated superior qualities, including a higher α-tocopherol content (138.8 mg/kg), and enhanced antioxidant capacities (DPPH polar: 12 μmol TE/100g, ABTS: 67.3 μmol TE/100g, FRAP: 112 μmol TE/100g), alongside a lower acid value (0.15 mg/g) compared to other blends. We highlight the roles of squalene, γ-tocopherol, δ-tocopherol, and fatty acid composition in enhancing the antioxidant capacity of oil blends. Specifically, the blend labeled F3 emerged as a nutritionally superior and highly antioxidative option among soybean oil blends. This study provides a framework for tailoring oil blends to meet specific nutritional and antioxidative requirements, providing a more flexible and purpose-driven approach to oil blending. • Innovative oil blending boosts nutritional quality. • Enhanced antioxidant capacity through natural methods. • The synergy of α-tocopherol, squalene, and fatty acids in blends boosts health benefits and stability.
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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 | 0.000 |
| 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".