Bean flour under pressure: Probing the techno-functionality through processing-structure-function analysis
Bibliographic record
Abstract
High pressure processing (HPP) was employed to modify the structural make-up of starch and protein in bean flour to ultimately alter the techno-functionality of the flour. Aqueous bean flour dispersions (20 % solid content (w/w)) were processed at pressures ranging from 250 to 600 MPa for 3 min. The results indicated that HPP modified the morphology of starch granules with a decrease in relative crystallinity as a function of pressure. Pressure-induced changes in the secondary structure of bean flour protein were indicative of protein denaturation. The effect of HPP on bean flour techno-functionality was noted in the Rapid Visco-Analyzer test, where a significant decrease in the peak viscosity was observed for flour processed at 600 MPa. The water absorption index of HPP (600 MPa) flour was higher than the one recorded for all other pressure processed flours. Conversely, pressure processing did not alter the water solubility index of the flours. • High pressure processing (HPP, >500 MPa) caused indentations in starch granules. • HPP (>500 MPa) increased the relative percentage of β-turn structure in bean protein. • Changes in starch and protein structure were pressure dependent. • HPP generally decreased the pasting viscosity of bean flour slurries and increased the water absorption index.
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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 | 0.001 |
| 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".