Comparative study on yellow pea protein extracted with deep eutectic solvent: A novel and eco-friendly solvent in food processing
Bibliographic record
Abstract
The growing demand for sustainable and efficient plant protein extraction methods has led to increased interest in alternative, eco-friendly techniques. Deep eutectic solvents (DES) have emerged as a promising green extraction technology, yet their impact on the structural, thermal, and techno-functional properties of yellow pea protein (YPP) remains underexplored. This study compares YPP extracted using DES (YPP-DES) with conventional methods, including salt extraction (SP), isoelectric precipitation (pH), and air clarification (AC), with commercial YPP (YPP-CO) serving as a reference. Protein purity (PP), yield (PY), and recovery rate (RR) ranged from 48.83% to 98.29%, 51.84% to 72.49%, and 13.23% to 19.70%, respectively (p<0.05). YPP-DES exhibited the highest PY and RR, while YPP-SP had the lowest. The denaturation temperature was lower in YPP-DES and YPP-AC (air-clarified YPP) compared to YPP-CO. Similarly, lightness was higher in YPP-AC compared to YPP-CO. However, yellowness was lower in both YPP-AC and YPP-DES. Emulsion activity index, emulsion stability index, and foaming capacities were superior in YPP-DES and YPP-SP compared to other methods (p<0.05). Regardless of the extraction method, freeze-dried YPP exhibited a sheet-like structure, whereas YPP-CO was spherical and hollow. These findings establish DES as a novel green extraction technique for YPP, achieving the highest PY and RR without compromising PP or techno-functional properties. 1. DES extraction enhanced yellow pea protein yield and recovery rate. 2. Techno-functional properties varied with extraction methods used. 3. Freeze-dried YPP showed a sheet-like structure, unlike commercial spray-dried YPP. 4. DES-extracted YPP retained high purity without compromising functionality. 5. DES proved to be a novel, green method for sustainable YPP extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".