Characterization of Whole Wheat Bread Reformulated with Pea and Soy Protein Isolates
Bibliographic record
Abstract
Aims: At present, there is an increasing demand for plant-based proteins due to their nutritional values and beneficial effect to the environment. Improving the availability of plant protein enriched foods would be highly beneficial for the consumers to follow a healthy diet. Reformulating a commonly consumed product, such as bread, with plant-based protein will be a convenient option for the people to improve their protein intake. Therefore, the objective of this study was to determine the effect of protein isolate (pea and soy) on color, microscopic structure, sensory properties, and protein content in whole wheat bread. Methods: The experiments were conducted at the University of Guelph, ON, Canada. Seven whole wheat bread samples (control, P20, P30, P40, S20, S30 and S40) were prepared (n = 3 loaves for each treatment). Results: The RGB and L*a*b*values of loaf (crust) and slice (crumb) of the control bread was higher than that of all protein enriched bread samples. The control bread had larger pores than the protein enriched samples. The pores in the pea protein isolate enriched bread were tightly packed while compared to soy protein. There was no difference in most of the sensory attributes between control and S20 breads. In terms of softness, all protein blended breads scored lower than the control. Fifty percentage of the panelists chose S20 as their first choice, whereas only thirty percent selected control as their first choice. The protein content of the bread samples was 8.9, 15.4, 18.1, 20.7, 15.9, 18.6 and 20.9%, for control, P20, P30, P40, S20, S30 and S40, respectively. Conclusion: Soy protein isolate has more opportunities than pea protein isolate to incorporate with whole wheat flour, and produce acceptable bread.
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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 | 0.000 | 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) | 0.000 | 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".