Heat-treated bean flour: Exploring techno-functionality via starch-protein structure-function analysis
Bibliographic record
Abstract
The study aimed to modify the structure of two major components (i.e., starch and protein) in bean flour to enhance bean flour's techno-functionality. The bean flour was processed by two very different temperature treatments – dry heat (DH) and extrusion. The effect of processing on starch and protein was analyzed by characterizing their physicochemical properties. While processing did not alter, as expected, the total starch (35–47 % (db)) and protein (24–25 % (db)) content of the bean flours, it did significantly change the microstructure and molecular architecture of starch and proteins. DH processing mainly affected protein conformation while minimally affecting starch structure. Conversely, extrusion caused extensive structural modifications of both starch and protein in bean flour. Both DH and extrusion processing decreased intramolecular β-sheet secondary structure of bean protein. Additionally, both unprocessed and thermally processed bean flours were characterized for their techno-functional properties. The heat treatments (DH and extrusion) did not only result in colour variations, but they also modified the particle size distribution, pasting profile, rheological properties, and water absorption and solubility indices of the bean flour. The structure-function relationship of bean flour components (starch and protein) and bean flour's functionality implied that thermal processing treatments led to changes in the protein/starch matrix which were reflected in the techno-functionality of processed bean flours. However, the changes induced by each of the different types of processing were very different, thus, unlocking different opportunities for functionalization of bean flours for food manufacturing and product development applications.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".