Nutrients in rice bran oil and their nutritional functions: a review
Bibliographic record
Abstract
Rice is an important food crop throughout the world. Rice bran, the outer layer of rice grain, is a by-product generated during the rice milling process. Rice bran oil (RBO) is extracted from rice bran and has also become increasingly popular. RBO is considered to be one of the healthiest cooking oils due to its balanced proportion of fatty acids, as well as high content of γ-oryzanol together with phytosterols, vitamin E, wax ester, trace and macro elements, carotenoids, and phenolics. The existence of these compounds provides RBO with various functions, including hypotensive and hypolipidemic functions, antioxidant, anticancer, and immunomodulatory functions, antidiabetic function, anti-inflammatory and anti-allergenic functions, hepatoprotective activity function, and in preventing neurological diseases. Recently, research on the nutrients in RBO focused on the detection of nutrients, functions, and processing methods. However, the processing and utilization of rice bran remain sufficiently ineffective, and the processing steps will also affect the nutrients in RBO to different degrees. Therefore, this review focuses on the contents and nutritional functions of different nutrients in RBO and the possible effects of processing methods on nutrients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".