Plant Extracts Inhibit the Formation of Hydroperoxides and Help Maintain Vitamin E Levels and Omega‐3 Fatty Acids During High Temperature Processing and Storage of Hempseed and Soybean Oils
Bibliographic record
Abstract
Oxidative stability of hempseed and soybean oils, was evaluated after heating at 180 °C, followed by a subsequent 14-day storage at 38 °C. Natural plant extracts (Rosemary, Sage, and Thyme) were added to oils, to evaluate the carry-over stabilizing potential. Heated oils exhibited a relatively faster (P < 0.05) onset of lipid oxidation, as depicted by the analysis of the peroxides and aldehydes formed during the lipid oxidation process, with hempseed oil being more susceptible to lipid oxidation than soybean oil. There were notable losses in ω-3 PUFA and ω-6-GLA during storage of heat treated hempseed oil (P < 0.05). Moreover, peroxide values measured from hempseed oil remained low after high-temperature heating but progressed at a relatively greater rate than that observed in soybean during storage (P < 0.05). The addition of different plant extracts to oils did not prevent oxidation due to heating, but effectively inhibited the generation of hydroperoxides during subsequent storage (P < 0.05). This stabilizing effect was attributed to retention of tocopherols, in particular, γ-tocopherol. PRACTICAL APPLICATION: This research demonstrates the use of plant extracts like rosemary, sage and thyme, for improving the shelf-life and nutritional stability of hempseed and soybean oil. We demonstrate the deterioration of fatty acid profiles and vitamin E levels in the oil on heating and subsequent storage, and show the efficacy of using plant extracts in slowing down these deteriorations. This research will be applicable in food industries using or producing oils for use in food during cooking, and also as dressing on already processed food products.
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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 | 0.001 | 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 | 0.001 |
| 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".