Comparative analysis of <i>Nigella sativa</i> L. oil extraction processes: impacts on chemical composition, antioxidant and antimicrobial properties
Bibliographic record
Abstract
This work intended to study differences in oil yield and co-extraction of antioxidant and antibacterial compounds of Nigella sativa L. seed oil as a function of two main extraction processes: mechanical pressing and solvent extraction, including hexane and chloroform. Next, we studied such oils’ chemical composition and antioxidant and antibacterial activities. Notably, the chemical cartelization was conducted by use of HPLC. The antioxidant activity of the oils was assessed using DPPH, TAC, and FRAP assays. Antimicrobial activity was investigated against bacteria and fungi with clinical importanceT. HPLC-DAD analysis identified some bioactive compounds in the oils, including thymoquinone, carvacrol, and gallic acid. Results showed that oils extracted by different methods exhibit varying levels of antioxidant activity, with a higher concentration of antioxidant compounds in chloroform-extracted oils. Antimicrobial tests revealed significant inhibitory effects of the studied oils on the growth of bacteria and fungi. In conclusion, this research has made it possible to characterize the oils extracted from Nigella sativa L. Seeds by determining their chemical composition and biological properties, notably their antioxidant, antibacterial, and antifungal activity. These results contribute to a better understanding of the potential benefits of these oils for human health and open up exciting prospects for their use in various fields, notably as food additives and therapeutic agents.
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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) | 0.001 | 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 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".