Functionality and nutritional value of faba bean protein isolates: Comparison to major legume proteins in the market
Bibliographic record
Abstract
The present research compared the functional and nutritional values of protein isolates of faba bean, yellow pea, and soy to investigate the utilization potential of Canadian faba beans as value-added ingredients in food formulations. In the first study, protein isolates of faba bean (FPI) cultivars Fabelle, Malik, and Snowbird, yellow pea (PPI) CDC Amarillo, and soybean (SPI) AAC 26-15 were prepared by alkaline extraction and isoelectric precipitation (AE-IP) and evaluated for their physicochemical and functional properties. The reduced water usage from the 1:10 flour:solvent ratio to 1:8 maintained the protein yield, whereas the ratio of 1:6 significantly lowered protein extractability. The faba bean seeds were rich in protein (29.34-34.35%), higher than that of pea (23.10%), while being equivalently low in lipids (1.11-1.35%). In summary of the quality attributes, with little impact of protein composition in terms of the legumin:vicilin (L/V) ratio, the functionalities (protein solubility, water and oil holding capacity, foaming and emulsifying properties) of FPI were mostly similar to those of PPI while being comparable or higher to those of SPI. The cultivar Snowbird had a few attributes generally different from those of Fabelle and Malik. In the second study, legume flours and isolates prepared in Study 1 were compared for their nutritional values. The faba bean samples were largely comparable to pea and soy for the content of total phenolic compounds, condensed tannins, and phytic acid. Meanwhile, they were less concentrated with the raffinose family oligosaccharides (RFO) and contained additional vicine and convicine at varying levels. However, since methionine and cysteine were more limiting in the faba bean samples, they had lower protein quality (in vitro protein digestibility corrected amino acid score; IV-PDCAAS) than pea and soy, especially for isolates (Snowbird < Fabelle < Malik) due to the loss of albumins during AE-IP. Overall, the findings suggested that FPI could replace PPI or SPI in formulating products that require additional or improved functionality, whereas when used as nutritional ingredients, a complementary blend with cereals would be beneficial. Future studies should investigate the modification of wet extraction processes to minimize nutritional losses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".