Unlocking hidden potential of rice bran: Enzymatic treatment for enhancing techno-functional properties
Bibliographic record
Abstract
Rice bran (RB) is a by-product with limited application due to technological constraints.Enhancing its technological functionality as potential food ingredient will improve the sustainability of rice production.The aim was to study the impact of enzymatic and thermal treatments on defatted rice bran using six distinct commercial enzymes (carbohydrases and proteases) and dry heating by evaluating its technological, nutritional and functional properties.Enzymatic treatment increased up to 208% the soluble dietary fiber content (8.19 g/100 g) of defatted RB.Moreover, the solvent retention capacity, including water, oil, sodium carbonate, and sucrose, exhibited a noteworthy increase across all treatments (p < 0.05).Bran color changed after treatments, increasing its luminosity (L*) and decreasing the value of a* in all cases, but b* decreased when treated with protein-acting enzymes while increased with carbohydrate-acting enzymes.Proteases played a pivotal role in reducing particle size and forming gels requiring minimal force for application.Microscopic analysis revealed that carbohydrasestreated samples exhibited prominent cell wall breakage, while protease-treated ones showed a gel-like surface with less distinct protein bodies and layered walls.These comprehensive study sheds new transformations brought about by these enzymatic interventions, offering valuable insights into the optimization of rice bran functionality.
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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 | 0.000 | 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".