Fractionation of the rice bran layer and quantification of vitamin E, oryzanol, protein, and rice bran saccharide
Bibliographic record
Abstract
Value-added processing with respect to rice milling has traditionally treated the rice bran layer as a homogenous material that contains significant concentrations of high-value components of interest for pharmaceutical and nutraceutical applications. Investigators have shown that high-value components in the rice bran layer vary from differences in kernel-thickness, bran fraction, rice variety, and environmental conditions during the growing season. The objectives of this study were to quantify the amount of rice bran removed at pre-selected milling times and to correlate the amount of rice bran removed at each milling time with the concentration of vitamin E, gamma-oryzanol, rice bran saccharide, and protein obtained. The ultimate goal of this research is to show that rice bran fractionation is a useful method to obtain targeted, nutrient-rich bran samples for value-added processing. Two long grain rice cultivars, Cheniere and Cypress, were milled at discrete times between 3 and 40 seconds using a McGill mill to obtain bran samples for analysis. Results showed that the highest oryzanol and protein concentrations were found in the outer portion of the rice bran layer, while the highest rice bran saccharide concentration was found in the inner portion of the bran layer. Vitamin E concentration showed no significant difference across the bran layer within a variety, though the highest magnitude of concentration occurs within the first 10 seconds of milling for both varieties. To extract the higher concentration of oryzanol and protein only the outer portion of the bran layer requires processing, while to extract the higher concentration of rice bran saccharide, only the inner portion of the bran layer requires processing. Rice bran fractionation allows for the selective use of portions of the bran layer and is advantageous for two reasons: (1) bran fractions contain higher concentrations of components of interest with respect to the overall bran layer average, and (2) less bran needs to be processed to obtain components of interest.
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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 | 0.001 |
| 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) | 0.001 | 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".