Investigation of Flavor and Functional Properties of Diverse Yellow Pea Ingredients for Pan Bread Applications
Bibliographic record
Abstract
Yellow peas (YPs) are a promising source of sustainable plant proteins however, characteristic off-flavors limit consumer acceptance and hinder their use as value-added food ingredients. This study evaluated the compositional and sensory characteristics of fourteen processed YP ingredients to determine their suitability in pan breads. Nine protein isolates (YPIs), three protein concentrates (YPCs), and two heat-treated flours (YP-IR and YP-RF) were analyzed for fatty acid composition, lipoxygenase (LOX) activity, volatile organic compounds (VOCs), and electronic nose (eNose) responses. LOX activity and linoleic acid, a key LOX substrate, differed significantly between YP ingredients. Fifteen VOCs previously linked to off-flavors were identified, with YPIs exhibiting the highest concentrations. eNose analysis correctly predicted YP ingredient identities with 69.5% accuracy and distinguished treated flours from other YP ingredients. Pan breads using two YPI ingredients (YPI-1 and YPI-2) predicted to produce strong off-flavors, the two heat-treated flours and a wheat flour (WF) control were prepared. Breads were evaluated for proximate composition, consumer acceptance, descriptive sensory attributes, and instrumental color. YPI-1 bread was significantly less acceptable overall than WF (p < 0.001) and showed higher pea aroma and flavor (p < 0.001). YPI-2 bread had a denser texture (p < 0.05) and significantly higher wheaty aroma (p < 0.01). Breads made with YP-IR and YP-RF performed closest to the WF control. The heat-treated YP flours demonstrated the greatest potential as pan bread additives. YPI samples were associated with undesirable sensory characteristics. Analyses of the raw YP ingredients effectively predicted sensory differences, highlighting the usefulness of these methods as screening tools during product development. PRACTICAL APPLICATIONS: This research shows that different value-added yellow pea ingredients can introduce a range of flavors into food matrices, and that flavoromics tools can assist with product development. The two heat-treated yellow pea flours were identified as the best options for bread making because they were less related to pea aroma and flavor in the final product. These findings can help bakers and food manufacturers choose pea ingredients that make high-protein breads taste better.
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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".