Yellow Field Pea Protein (Pisum sativum L.): Extraction Technologies, Functionalities, and Applications
Bibliographic record
Abstract
L.) hold significant value for producers, researchers, and ingredient manufacturers due to their wealthy composition of protein, starch, and micronutrients. The protein quality in peas is influenced by both intrinsic factors like amino acid composition and spatial conformations and extrinsic factors including growth and processing conditions. The existing literature substantiates that the structural modulation and optimization of functional, organoleptic, and nutritional attributes of pea proteins can be obtained through a combination of chemical, physical, and enzymatic approaches, resulting in superior protein ingredients. This review underscores recent methodologies in pea protein extraction aimed at enhancing yield and functionality for diverse food systems and also delineates existing research gaps related to mitigating off-flavor issues in pea proteins. A comprehensive examination of conventional dry and wet methods is provided, in conjunction with environmentally friendly approaches like ultrafiltration and enzyme-assisted techniques. Additionally, the innovative application of hydrodynamic cavitation technology in protein extraction is explored, focusing on its prospective role in flavor amelioration. This overview offers a nuanced understanding of the advancements in pea protein extraction methods, catering to the interests of varied stakeholders in the field.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".