Optimizing pretreatment and extraction methods for high-quality Cinnamomum camphora seed oil: Impacts on bioactive components and antioxidant capacity
Bibliographic record
Abstract
This study for the first time systematically investigates the combined effects of shelled/unshelled pretreatments and three extraction methods (cold pressing, CP; hexane extraction, HE; and aqueous enzymatic extraction, AEE) on physicochemical properties, nutritional components, and antioxidant capacity of Cinnamomum camphora seeds oil (CCSO) quality, addressing gaps in synergistic optimization of bioactive retention in CCSO. Lipid yield varied from 37.85 % to 94.26 %, with CP producing the highest total tocopherol content (shelled oil: 328.30 mg/kg and unshelled oil: 291.80 mg/kg) and total phenolic content (TPC, shelled oil: 33.78 mg/kg and unshelled oil: 48.24 mg/kg), as well as the strongest antioxidant activity by DPPH (shelled oil: 109.58 μmol TE/100 g and unshelled oil: 130.95 μmol TE/100 g) and FRAP (shelled oil: 1258.56 μmol TE/100 g and unshelled oil: 1612.33 μmol TE/100 g) assays. In contrast, HE produced lower levels of β-sitosterol, TPC, and squalene, while AEE yielded higher concentrations of monounsaturated fatty acids and β-sitosterol. Shelling increased tocopherol and β-sitosterol contents, but decreased unsaturated fatty acid, TPC, and antioxidant ability. Hierarchical cluster analysis demonstrated that processing method significantly influenced oil properties. Regression and principal component analyses revealed a positive relationship between antioxidant capacity and TPC as well as C18:2. It was determined that Unshelled-CP represented the optimal strategy for producing high-quality CCSO, with strong antioxidant capacity (DPPH-polar: 23.17; DPPH-nonpolar: 43.17; DPPH-oil: 130.95; FRAP: 1612.33 μmol TE/100 g) and high levels of bioactive compounds, making it suitable for food, nutraceutical, and sustainable applications.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".