Composition, functionalities, and digestibility of proteins from high protein and normal pea ( <i>Pisum sativum</i> ) genotypes
Bibliographic record
Abstract
Abstract Although a lot of research has been focused on the applications of pea protein, the effects of genotypes on protein applications have not been sufficiently investigated. Three high protein genotypes and four normal genotypes were included in this study. The results showed that the pea proteins from these seven genotypes differed widely in 11S/7S ratio. P1141 and Lacombe had the highest 11S/7S ratio while P1142, P0540, and Cooper had the lowest. Since the three high protein genotypes were selected from different parent lines, they had different 11S/7S ratios, which may partially explain their various functionalities. This demonstrated the potential of using breeding as a tool to manipulate the 11S/7S ratio of pea protein. The 11S/7S ratio may play a more important role in determining the protein functionalities than high/normal protein level in the seed. The solubilities of all seven samples were pH dependent. At pH 7, Lacombe and P1141 had the lowest solubility among all the tested samples, which may be a result of their high 11S/7S ratio. The proteins from all genotypes showed comparative water and oil holding capacities. P1141, Lacombe and Earlystar showed excellent emulsifying and foaming capacity at all tested pHs, which may be attributed to their relatively higher 11S/7S ratio. The in vitro digestibility varied among genotypes regardless of high protein or normal genotypes but all higher than 75%. The results of this study demonstrated that breeding could manipulate the pea protein content and composition, which further determined its functional properties and applications in food products.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".