Pea, Lentil and Chickpea Protein Application in Bread Making
Bibliographic record
Abstract
The objective of the present study was to determine if wheat flour could be successfully substituted with lentil, pea, and chickpea (pulses) proteins in bread making and to study the characteristics of the breads produced. Results of the study showed that addition of pulse proteins affected bread mass volume, color and hardness. The highest bread mass volume (4.27 ± 0.07 mL/g) was obtained with the control (unsupplemented) bread. Mass volumes decreased at the 3% supplementation level for all supplemented breads and no significant differences (P>0.05) were observed between the pulse proteins. At the 6% and 9% supplementation levels, significant differences were observed between the mass volumes of the breads. Chickpea protein gave the highest mass volume at both the 6% and 9% supplementation levels (3.72 ± 0.21 and 3.84 ± 0.27 mL/g, respectively) followed by lentil protein (3.43 ± 0.19 and 3.43 ± 0.07 mL/g, respectively). Breads supplemented with pea protein generally had the lowest mass volume. Bread crumb and white became darker as supplementation level was increased and in the lentil supplemented bread, a greener color appeared at the 6% and 9% supplementation levels. Hardness of the bread white for all supplemented samples was close to the control at the 3% supplementation level but significantly increased at the 9% supplementation level. Overall, pea proteins had the most significant effect on bread hardness and mass volume whereas chickpea protein concentrate showed the greatest potential for use as an ingredient in bread making.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".