Impact of Trehalose as a Partial Replacement of Sugar on Physicochemical Properties of Brown Rice Tofu during Storage
Bibliographic record
Abstract
Trehalose has been used in the foods, pharmaceuticals, and cosmetics industries due to its multifunctional properties. The aim of this study was to elucidate the effect of trehalose as a partial replacement of sugar on the physicochemical properties of the brown rice tofu during storage. As a result, the L* values for the brown rice tofu without trehalose addition and with the application of 1% trehalose decreased significantly with the passage of the storage day. In contrast, 10% trehalose replacement provided a marked positive effect on the color retention of the brown rice tofu, especially whiteness index, suggesting the prevention of the browning of the brown rice tofu. The water content of the brown rice tofu with incorporation of 10% trehalose was significantly high when compared with other tested brown rice tofu after 7 days of storage, resulting in suppression of the hardening of the brown rice tofu. In addition, the incorporation of trehalose could be suppressed the microbial growth on the brown rice tofu. This study proved that a partial replacement of sugar with trehalose, especially 10% trehalose, could be beneficial on the prevention of the quality degradation and improvement of the shelf life, such as prevention of the browning and suppression of the hardening and microbial growth of the brown rice tofu during storage.
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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) | 0.001 | 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".