Characteristics of Rice Flour Breads Using Yeast Isolated from Pear Red Bartlett Fruits
Bibliographic record
Abstract
To develop high qualities of rice flour breads, we tried to prepare breads using rice flours from major five non-glutinous rice cultivars on market shares of Japan and wild-type Saccharomyces cerevisiae strain YTPR1 isolated from pear Red Bartlett fruits. Apparent amylose contents of rice flours were measured about 12.1-19.9%. Damaged starch contents of these flours were about 22% regardless to the kinds of rice cultivars. Gluten was added about 20 wt.% based on rice flour, and breads were made in the same way. Any bread has caused caving. Next, the amount of water added in dough was regulated in consideration of moisture contents of rice flours. Except for Akitakomachi flour, breads largely expanded, although loaves volumes did not amount to that on bread made from bread flour and commercially available baker’s dried yeast. It was observed correlation between the amount of water and amylose contents of rice flours with R2 = 0.703. It suggested that the amount of water added in dough might estimate from amylose contents of rice flours. Specific volumes of these loaves were low compared with that made from bread flour. However, by sensory analysis, breads made from Hinohikari and Haenuki flours had total points closest to that made from bread flour: it could produce high quality of breads using Hinohikari and Haenuki flours and yeast isolated from pear Red Bartlett fruits.
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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".