Enrichment and recovery of pea (Pisum sativum L.) proteins using foam fractionation for simultaneous enhancement of their functional properties
Bibliographic record
Abstract
• Foam fractionation concentrated and purified proteins from pea flour. • First stage fractionation improved protein recovery; second stage improved enrichment. • Freeze-dried foamate had higher crude protein content than the pea protein flour. • Foamed proteins had improved functional properties. Peas ( Pisum sativum L.) are an extensively grown export crop in Canada. Pea protein flour (PPF) produced from milled peas typically consists of 19.8 % protein (dry weight basis). The separation and concentration of proteins from pea flour can be made more cost-effective through foam fractionation, which may also impact functional protein properties. This study investigates foam fractionation as a technique for the enrichment and recovery of pea flour proteins from a solution using a two-stage foam fractionation process and evaluates the functional properties of the recovered proteins in the foamate. Process parameters such as protein concentration in the starting solution ( C ), pH, air flow rate ( V ) and liquid loading volume ( L ) on protein recovery percentage ( R , %) and enrichment ratio ( E ) were studied. First stage foam fractionation achieved a high protein recovery R (%) 86.66 ± 3.55 %, with E of 2.106 ± 0.180 at C of 1.05 g/L, pH 4.5, V of 800 mL/min and L of 4000 mL. Second stage fractionation achieved a high protein enrichment E of 5.83 ± 0.14 at pH 4.5, V of 300 mL/min and L of 1500 mL. Foamate powder consisted of similar proteins with higher purity than the pea protein flour and had improved functional properties, such as soluble content (at pH 3, 7, and 8), foaming, emulsification, and oil and water holding capacities. Thus, foam fractionation can recover and purify pea protein fractions from dilute solution and simultaneously improve their nutritional value and functional properties for application as novel ingredients in food systems.
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 | 0.000 |
| 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".