Functional properties of faba bean proteins extracted by different aqueous processes for food applications
Bibliographic record
Abstract
Dry fractionation of faba bean protein is a sustainable alternative to energy-intensive wet fractionation approaches. However, it can only lead to relatively modest enrichment in protein content. The primary goal of this study was to compare the impact of aqueous protein extraction processes on the functionalities of faba bean proteins for food applications. Proteins from two Canadian faba bean cultivars Snowbird (zero-tannin, ZT) and Athena (high-tannin, HT) were extracted by dialysis following water extraction (W) and salt extraction (S) processes, and conventional alkali-acid approach (A). Although salt-soluble globulins were the primary proteins found in faba beans based on Osborne's protein classification, protein isolates (PIs) from ZT-W and HT-W had significantly higher (P< 0.05) protein contents on a dry matter basis (89.8±0.4% and 92.0±0.0%, respectively, Nx6.25) as compared to protein concentrates (PCs) from ZT-S (78.2±0.4%) and HT-S (77.7±0.2%). These differences in protein extractability could be attributed to the higher levels of naturally present minerals. Substantially lower (P< 0.05) mineral contents were detected in HT in comparison to ZT, plausibly due to the affinity of tannins towards minerals. Calorimetric analysis of W-PIs and A-PIs maintained at low-moisture contents resulted in a very high denaturation temperature range (225-235°C), implying their thermal stability for high-temperature processing. Furthermore, solubility, foaming and emulsification properties, and hydration capacities of W-PIs were higher or comparable to those of A-PIs and S-PCs. Dynamic rheological studies (25€“95€“25 °C) of W-PI heat-induced gels indicated that storage modulus (G') and loss modulus (G'') increased over time with an early crossover point (G' > G'') as compared to A-PIs. The stress and strain at fracture of W-PIs and A-PIs gels were comparable to those of whole egg gels. In summary, W-PIs were superior to S-PCs in terms of their functionalities and can be considered chemical-free alternatives to A-PIs, for sustainable food applications.
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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 | 0.000 | 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 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".