Laboratory-Scale Characterization of a Purple Rice Variety: Examination of Milling Quality and Determination of Anthocyanin and Oil Concentration across the Bran Layer
Bibliographic record
Abstract
Assessment of rice milling quality for new rice varieties and characterization of phytochemicals within the rice bran layer provide important information for optimization of milling and evaluation of the extraction potential for value-added products. In this study, we characterized the milling quality of a purple rice variety (line number MCR02-1576) and measured oil and anthocyanin concentration in the bran layer. After shelling, samples were milled for different lengths of time with a McGill mill and assessed for degree of milling (DOM), whiteness, transparency, milling recovery, and the amount of bran removed at specific milling times. Bran samples were evaluated for anthocyanin and oil concentration. Results showed that this rice variety exhibited low milling recovery (<50%), whiteness (<15%), and transparency (<1%) when compared to non-pigmented rice varieties. The whiteness and transparency values indicate that purple pigment is present in the kernel as well as the bran layer, unlike most other purple rice varieties. DOM was not measurable. Anthocyanin concentration increased linearly with milling length across the entire bran layer. Oil concentration also increased linearly across the inner bran layer. The mean inner bran layer oil concentration was 22%. Processing to obtain the inner fraction of the bran layer for this variety maximizes anthocyanin and oil recovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".