Effects of sonication on the in vitro digestibility and structural properties of buckwheat protein isolates
Bibliographic record
Abstract
The present work investigated the effects of sonication at different amplitudes and durations on the in vitro digestibility of buckwheat protein isolates (BPIs). The conformation, particle size and microstructures of the BPIs were also studied to explicate the possible mechanisms of the sonication-induced changes. The results showed that sonication conditions of 20 kHz, pulsed on-time 10 s, off-time 5 s, amplitude of 60% and duration of 10 min (SA6T10) improved the digestibility of BPIs from 41.4% (control) to 58.2%. The tertiary structure analysis showed that sonication exposed the hydrophobic core buried inside the protein molecules and broke the intramolecular crosslinks, based on the increase in the surface hydrophobicity and intrinsic fluorescence and the decrease in the disulphide content. The secondary structure analysis showed that SA6T10 decreased the content of β-turn and β-sheet by 40.9% and 22.4%, respectively, and increased the content of anti-parallel β-sheet, random coil, and α-helix by 40.9%, 30.6%, and 25.5%, respectively. The particle size of the control BPIs (427.7 ± 76.7 nm) increased to 2130.8 ± 356.2 nm in the SA6T10 sonicated sample with a corresponding decrease in the polydispersity index from 0.97 ± 0.04 to 0.51 ± 0.13. Moreover, scanning electron microscopy indicated that sonication broke the macroparticles into smaller fragments and changed the surface state of the proteins. Taken together, sonication has proven to be a promising approach for improving the digestibility of buckwheat proteins, which can be explored as a source of plant-based alternative protein 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".