Quality Improvement and Characterization for Production of Acceptable High-Quality Brown Rice Tofu in Japan
Bibliographic record
Abstract
The aim of this study was to improve the quality of brown rice tofu to produce it with a superior-quality. When the brown rice flour was heat treated with water, the water absorption rate of flour decreased using brown rice flour with a particle size range of < 212 μm when compared with that of brown rice flour with a particle size range of < 475 μm. The cohesiveness and gumminess of brown rice tofu made from brown rice flour with a particle size range of < 212 μm were fairly high in comparison with those of brown rice tofu made from brown rice flour with a particle size range of < 475 μm. In addition, the adhesiveness and cohesiveness of brown rice tofu remarkably decreased when heating and kneading times of brown rice flour dough were extended. By textural and sensory analyses, it became clear that the use of brown rice flour with a particle size range of < 212 μm and the extension of gelatinization time and heating and kneading times of the dough were important factors for preparation of high-quality brown rice tofu. Therefore, the results indicated that it could produce acceptable high-quality brown rice tofu having smooth and new palate feeling while suppressing adhesiveness and stickiness peculiar to rice flours.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".