Nutritional Composition and Volatile Compounds of Black Cumin (Nigella sativa L.) Seed, Fatty Acid Composition and Tocopherols, Polyphenols, and Antioxidant Activity of Its Essential Oil
Bibliographic record
Abstract
This study was to assess the nutritional quality and bioactive properties of black cumin (Nigella sativa L.) seeds and oil commonly found in the Chinese market. The results showed that black cumin seeds contain 5.02, 21.07, 39.02, 3.02, 6.01, and 25.86% moisture, crude proteins, crude fat, ash, fiber, and carbohydrates, respectively. It also contains substantial amounts of minerals, namely calcium, potassium, phosphorus, magnesium, sodium, iron, zinc, and copper. Glutamic acid (4.10 g/100 g protein) is the major amino acid of black cumin seeds. The major volatile components in black cumin seeds were thymoquinone (21.01%), o-cymene (18.23%), and β-thujene (17.22%). Cumin seed oil extracted by the soxhlet method contains high quantities of unsaturated fatty acids (UFA; 85.16%) and low amounts of saturated fatty acids (SFA; 15.02%). The major fatty acid of black cumin seed oil was linoleic acid (57.71%), followed by oleic acid (24.46%). The most prominent TAG of black cumin seed oils was oleoyl-dilinoleoyl-glycerol (OLL; 38.87%). In addition, the levels of α-tocopherol, β-tocopherol, γ-tocopherol, and total polyphenols in the black cumin seed oil were 25.59, 14.21, and 242.83 mg/100 g, and 315.68 mg GAE/kg, respectively, and possessed high antioxidant activity (DPPH IC50%, of 4.02 mg/mL). These findings demonstrate that black cumin seeds are nutritionally rich with high potential applications in the food, pharmaceutical, and cosmetic industries.
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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 | 0.001 | 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 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".