Application of EPR Spectroscopy and DSC for Oxidative Stability Studies of <i>Nigella sativa</i> and <i>Lepidium sativum</i> Seed Oil
Bibliographic record
Abstract
Abstract Food products incorporating oil rich in polyunsaturated fatty acids are known to become easily rancid. In order to delay the process of oxidation there is a need to develop analytical methods that accurately estimate the antioxidant potential of the oil and predict its oxidative stability. Thus, the present work investigated Lepidium sativum and Nigella sativa seed oil as a source of natural antioxidants. Further, the effect of blending the two oils on the oxidative stability of the unsaturated L. sativum oil was also studied. Electro‐paramagnetic resonance (EPR) spectroscopy showed 100 % 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radical quenching by both the oils at a concentration of 5 % w/v oil in benzene thereby establishing the anti‐oxidant potential of both the oils. Differential scanning calorimetry (DSC) studies showed addition of N. sativa oil to L. sativum oil enhanced its oxidation onset temperature, a relevant indicator of oil stability, thereby making N. sativa a source of natural antioxidants. Consequently, the DSC and EPR spectroscopy techniques so developed can find application in shelf life studies of oil.
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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) | 0.000 | 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".