High‐ and Low‐Protein Pea Genotypes: A Comparative Study of the Physicochemical, Functional, and Quality Properties of Protein Isolates
Bibliographic record
Abstract
ABSTRACT Background and Objectives This study evaluated the effect of seed protein content on the composition, physicochemical, functional, and quality properties of pea protein isolates recovered through alkaline extraction/isoelectric precipitation. Samples were divided into three high‐protein lines (HPL), three low‐protein lines (LPL), and one control (medium protein). Findings Isolates derived from HPLs had higher protein (92.4%–94.5%) compared to LPLs (79.9%–88.7%) and the control (~92%). The protein content was significantly correlated to the legumin/vicilin ratio (1.0–1.8), surface charge (−38 to −23 mV), and surface tension (49–52.7 mN/m). No significant correlation was observed between protein content and functionality. Minor variation was observed for emulsifying (17.7–22.1 m 2 /g) and foaming (160%–215%) properties. LPLs presented higher amino acid score (0.83), and In Vitro protein digestibility corrected amino acid score (~0.72) than HPLs (~0.72 and ~0.64, respectively). Protein digestibility was in the range of ~87% for all lines. Conclusions Seed protein concentration impacted the pea protein isolates' proximate composition, quality, and physicochemical/surface properties. HPLs presented higher total protein content and LPLs presenting overall higher protein quality, whereas functionality did not differ significantly between HPLs versus LPLs. Significance and Novelty Our findings highlight the importance of sourcing and selecting pea lines tailored to specific applications, with a trade‐off between protein quantity versus quality.
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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".