High pressure processing at different hydration levels as a tool to enhance rice bran stability and techno-functionality
Bibliographic record
Abstract
• HPP is effective as preservative treatment at hydration levels higher than 15% • Hydration level and HPP enhance techno-functional characteristics of rice bran. • Hydration of 30% enhances the impact of HPP on rice bran structure. • HPP reduces peroxide values at low hydration and increases them at high hydration. • HPP achieves stable emulsions across all hydration conditions. High-pressure processing (HPP) enhances food safety and shelf life by inactivating microorganisms and preserving food quality, yet its effectiveness in low-humidity environments has not been evaluated. This study investigated the effects of HPP at 500 MPa for 15 min across varying hydration levels (15, 30, 60, 77 %) on rice bran (RB), aiming to identify microbial effectiveness, besides techno-functional and physicochemical properties. HPP effectively reduced mesophilic bacteria, molds and yeast of RB at > 15 % hydration level, achieving reductions of up to 4 logarithmic cycles in the latter, nearing the detection limit of the method. However, it did not significantly impact spore inactivation. HPP treatment of ≥ 30 % hydrated RB induced particles aggregation and a honeycomb formation. The interaction between hydration and HPP treatment significantly affected the distribution of total dietary fibers, with an increase in soluble dietary fiber from 8.73 g/100 g to 11.03 g/100 g after HPP treatment at 15 % hydration level. Protein solubility was enhanced by hydration (15, 30 and 60 %), and peroxide values decreased after HPP treatment at low hydration (≤30 %) but increased when applied to high hydrated (>30 %) RB. Emulsifying activity decreased upon HPP treatment of highly hydrated RB (≥60 %), but more stable emulsions were achieved after HPP, regardless of the hydration level. Therefore, this study highlights the potential of HPP as a sustainable approach to enhance the utilization of rice bran in food applications, addressing existing knowledge gaps regarding its processing under different moisture conditions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".