Effects of Degree of Milling on Nutritional and Edible Quality of High-Resistant Starch Rice
Bibliographic record
Abstract
The aim of this study was to investigate the effects of milling degree on nutrient content, especially resistant starch (RS), and edible quality of starch rice variety Youtangdao3 (YTD3) with high RS. High RS rice variety YTD3 was processed with different milling times (0, 10, 20, 30, and 40s) to evaluate the effects on nutritional components, mineral content, vitamins, pasting properties, and cooking/eating quality. With the increase in milling time, the average milling reduction rate increased from 0 to 8.54, 11.81, 13.27, and 16%, which correlated with a decrease in the yield of head rice, crude fat, crude protein, and ash content. Minerals and vitamins, especially vitamin E and B vitamins, decreased significantly with increasing milling time. Sensory analysis revealed a decrease in rice hardness and an increase in viscosity with longer milling, suggesting improved palatability. However, excessive milling reduced the nutritional value of the rice. The study highlights the need for an optimal balance between the degree of milling (DOM) and the preservation of nutritional value to improve the overall quality of rice products. The results are important for guiding rice processing practices to maintain the health benefits of RS while preserving the sensory appeal of rice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".