High- and Low-Protein Pea Genotypes: Effect of Seed Traits on the Properties of Flours and Protein Isolates
Bibliographic record
Abstract
With the rising interest in pea proteins within the food industry, enhancing protein concentration and improving its nutritional quality through plant breeding has garnered special attention. The development of new varieties with enhanced protein quality and concentration, and their application in food products, requires a comprehensive analysis of the underlying genetics that impact the techno-functional and quality attributes of pea ingredients. Similarly, seed shape (particularly the underlying mutation in the Starch-branching enzyme I) can impact the chemical composition of pea varieties and further influence nutritional and functional attributes. Therefore, this research aimed to investigate the effect of seed protein concentration and seed shape on the physicochemical, functional, and nutritional quality properties of pea flours and protein isolates (PPI). New pea lines containing high- and low-protein concentrations, as well as a round pea line (RPL), were developed in the Crop Development Centre (CDC), University of Saskatchewan. In addition, wrinkled pea accessions (WPA) were obtained from USDA germplasm system (Pisum collection). CDC Meadow was used in all studies as a round-shaped and medium-protein content control. High- (HPLs) and low-protein lines (LPLs) from pea recombinant inbred line (RIL) populations PR-25, PR-30, and PR-31 were evaluated for protein-related traits. Accessions PI 210561, PI 261636, and PI 285722 from the USDA Pisum collection were selected for their wrinkled seed shape. In the first study, HPLs and LPLs were processed into flours and analyzed. Compared to LPLs, HPLs had higher protein content (21.9~28.2% vs. 18.7~21.7%) and lower starch content (41.2~45.7% vs. 46.3~51.2%). In addition, flours from HPLs presented ~8% more protein compared to the control, indicating breeding advancements in the analyzed populations. The functionality of the flours were not strongly correlated to the level of protein content in the pea lines. In terms of their nutritional quality, LPLs presented overall higher amino acid (AA) scores (0.91~1.21 vs. 0.84~1.03) and in vitro digestibility (0.72~0.98 vs. 0.67~0.82) compared to HPLs. In the second study, similar trends were observed after extraction for PPIs, in terms of composition, functionality, and protein quality. The protein content of HPLs was higher compared to LPLs (88.9~91.6% vs. 77.7%~85.4%), indicating that more protein can be fractionated from HPLs, presenting an advantageous resource for protein industries. A low correlation was observed between protein content and some functional parameters (e.g, foaming and emulsifying). Quality attributes of LPLs PPI were also higher compared to HPLs, as observed for the flours. In regard to the third study (seed shape), WPA flours showed higher protein content (24.3~27.1% vs. ~20.5%) and lower starch content (~32% vs. ~46%) compared to RPL. The opposite was observed in the PPI, with RPL exhibiting higher protein content compared to WPAs (~88% vs. ~84%), which was hypothesized to be due to variations in the starch composition and protein-starch interactions within the seed/flour of the different genotypes. Seed shape was not an influence on the functional properties of the flours and PPI; therefore, no obvious trend between RPL vs WPAs could be delineated in terms of functionality. Shape was a significant parameter in protein nutritional quality, with overall RPL showing higher in vitro digestibility and AA score than WPAs in both flours and PPI. Overall, this research explored the effect of seed quality traits (protein concentration and shape) on the functionality and nutritional properties of flours and PPI. Knowledge from these studies can contribute to pea breeding outcomes for improved quality traits of pea lines, as well as to protein fractionation processors, i.e., the importance of sourcing and selecting pea lines as raw materials tailored to specific applications rather than adopting a “one-size-fits-all” approach.
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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".