Impact of germination on the techno‐functional properties, nutritional composition, and health‐promoting compounds of brown rice and its products: A review
Bibliographic record
Abstract
Rice is a popular grain and forms part of the daily diet of people throughout the world. However, the consumption of rice and its products is sometimes limited by its high glycemic index due to its high starch content, low protein content and quality, and low bioavailability of minerals due to the presence of anti-nutritional factors. This has partly stimulated research interest in recent times toward the use of bioprocessing techniques such as germination as cheap and natural means to improve the nutritional quality, digestibility, and health properties of cereals, including rice, to partially achieve nutrition and food security in the developing regions of the world. This review highlights the impact of germination on the nutritional quality, health-promoting properties, and techno-functional characteristics of germinated brown rice grains and their products. The review demonstrated that germinated rice grains and their products have improved nutritional quality and digestibility, modified functional properties, and showed antioxidant, anti-inflammatory, anti-diabetic, anti-obesity, anti-cancer, and anti-cardiovascular activities. Germination appears to be a suitable bioprocessing method to improve the nutritional quality and bioactive constituents and modify the techno-functional properties of rice grains for diverse food applications and improved global nutrition and food safety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".