Enzyme-assisted Aqueous Extraction of Oil from Rice Germ and its Physicochemical Properties and Antioxidant Activity
Bibliographic record
Abstract
Enzyme-assisted aqueous extraction of rice germ oil (RGO) was performed in this study. The physicochemical properties, fatty acid composition, bioactive substances and antioxidant activity of RGO were analyzed. An enzyme composed of alcalase and cellulase (1:1, w/w) was found to be the most effective in the extraction yield of oil. The optimal oil yield of 22.27% was achieved under the conditions of an enzyme concentration of 2% (w/w), incubation time of 5 h, incubation temperature of 50°C, water to seed ratio of 5:1, and pH 6.0. The predominant fatty acids of RGO were oleic acid (39.60%), linoleic acid (34.20%) and palmitic acid (20.10%). The total saturated fatty acid (SFA), monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) composition of RGO were 22.50%, 39.60% and 36.00%, respectively. RGO yielded a high content of γ-oryzanol (530 mg/100 g oil), tocotrienol (62.96 mg/100 g oil), tocopherol (23.24 mg/100 g oil) and a significant amount of phytosterol (372.14 mg/100 g oil). It exhibited notable antioxidant activities with IC50 values of 32.37 and 41.13 mg/mL, according to the DPPH radical scavenging assay and β-carotene/linoleic acid bleaching test, respectively.
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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 | 0.001 |
| 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) | 0.000 | 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".