Enzyme-assisted extraction of navy bean protein from whole and dehulled flour: Effects on extractability, structure, and techno-functional properties
Bibliographic record
Abstract
The growing demand for sustainable plant proteins calls for alternative extraction methods that preserve protein quality. Navy beans, though rich in hypoallergenic, gluten-free protein, remain underutilized. Conventional extraction methods often cause protein denaturation and aggregation, thereby limiting functional properties. This study evaluated enzyme-assisted (E) and homogenization-enzyme-assisted (HE) extraction of whole (W) and dehulled (D) navy bean flours (protease, pH 10, 0.5 %, 1.5 h), compared with aqueous extraction (Aq). Both extraction method and flour type significantly influenced extractability, structural, and techno-functional properties. HE-D exhibited the highest protein purity (68.99 %), while E-D and HE showed the highest recovery. Compared to Aq-W, solubility improved by 4.5 % in E-W. Enzyme-based extracts showed higher EAI than aqueous controls, with no separation among enzyme-treated groups; ESI varied depending on extraction method and sample matrix, with HE-D yielding the highest values. WAC increased by 32 % in HE-W, whereas OAC declined across enzyme-treated samples. LGC increased from 12 % to 18 % in enzyme-treated flour. Free sulfhydryl content increased in E and HE, whereas foaming ability and surface hydrophobicity decreased. SEM revealed disrupted aggregates and porous structures, while FTIR showed reduced β-sheet and α-helix with increased random coil and β-turn. XRD provided qualitative context on short-range order, and DSC revealed lower denaturation temperatures and enthalpies. Fluorescence and UV-vis spectroscopy indicated partial unfolding in enzyme-treated samples. TNBS primary-amine index (h) supported interpretation of structure-function trends. These findings support enzyme-based extraction as a viable green approach to enhance the structural and functional quality of navy bean proteins for 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".