Development of Soy‐Based Bread with Acceptable Sensory Properties
Bibliographic record
Abstract
UNLABELLED: Consumption of soy protein has been associated with benefits related to numerous areas of health. Due to the widespread consumption of bread, one means of contributing to the health of individuals is through the incorporation of soy protein into bread. To this end, soy flour (SF) or soy protein isolates (SPIs) in 20% and 12% substitution levels, respectively, were added to flour during bread manufacture. The developed breads were tested using a consumer panel for acceptability, using a refined white bread as a control. These data were compared to attribute intensity data collected by the trained panel to identify specific flavor and texture characteristics affecting liking. The sensory profile of the 20% SF bread was acceptable and comparable to the control bread, despite a significantly stronger beany flavor. No significant differences in sensory properties of the SF and control breads were detected by the trained panel for many sensory attributes. The SPI bread, however, had a sensory profile that was significantly more firm, dense, sour, beany, bitter, and astringent with a strong aftertaste in comparison to the wheat control bread. Consumer liking scores for the SPI bread was significantly lower than the liking of the control and the SF added bread. PRACTICAL APPLICATION: Many soy-enriched foods, while contributing positively to health, are considered unacceptable by consumers. This is due to negative sensory properties, such as beany, painty, and astringent notes, often perceived by consumers. This study provides information on the level of SF that can be included in bread in an amount that does not detract from consumer acceptability. This level also allows for a Food and Drug Administration (FDA) health claim to be made.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".