Sensory and Physical Characteristics of Pan Bread Fortified with Thermally Treated Split Yellow Pea ( <i>Pisum sativum</i> L.) Flour
Bibliographic record
Abstract
Pulses, including peas, are a good source of protein, dietary fiber, folic acid, and iron and are reported to reduce the risk for cardiovascular disease and diabetes. However, pulse ingredients present a known challenge as they exhibit a grassy/beany off-flavor. Heat treatment in some cases can decrease this off-flavor. The objective of this study was to determine the effect of substitution of 20% split yellow pea (SYP) flour treated by Revtech thermal processing at 140 °C with 10% steam (RT10%) and without steam (RT0%) for wheat flour in bread on the sensory attributes, acceptability, nutrient composition, firmness, color, and pH. RT10% was more acceptable overall than bread with untreated pea flour (USYP) or RT0% as assessed by 110 consumers. Sensory attributes were defined and measured on 15-cm line scales by an 11 member trained panel. Attributes associated with RT10% included wheaty, sweet, and yeast aromas and wheaty flavor, whereas attributes associated with USYP and RT0% were pea and nutty aroma and flavor. Although firmness and dryness were higher in RT10%, the acceptability of the bread texture was not affected. This sample contained significantly higher protein and lower carbohydrate than the wheat sample. PRACTICAL APPLICATION: Revtech (RT), a novel thermal process, when applied at 140 °C with steam to split yellow pea (SYP) flour successfully increased the acceptability of white pan bread fortified at 20% compared to bread fortified with RT 140 °C with no steam, and untreated SYP flours. This could be due to its association with wheaty aroma and flavor attributes rather than the pea aroma and flavor attributes of the other two breads.
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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 | 0.001 |
| 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".