EFFECTS OF HIGH PRESSURE PROCESSING ON SOYBEAN BETA‐CONGLYCININ
Bibliographic record
Abstract
ABSTRACT Effects of high pressure processing on conformational changes of soybean β‐conglycinin was studied by means of sulfhydryl groups detection, spectrofluorimetry, ultraviolet difference spectra, circular dichroism and electrophoresis. Significantly more sulflhydryl groups as well as hydrophobic regions and amino acid residues, which had ultraviolet absorbance had been found after high pressure processing (≥300 MPa). The CD analysis indicated that some of the ordered structures ofα‐helix andβ‐structure were destroyed and converted to random coil after processing at 500 MPa for 10 min. Electrophoresis analysis revealed that β‐conglycinin could be denatured and might be dissociated into subunits after high pressure processing (≥300 MPa). PRACTICAL APPLICATIONS The use of high pressure for food processing is now getting an increasing interest in the food industry because of the consumer's demand for convenience foods of the highest quality in terms of natural flavor and taste, and which are free from additives and preservatives. It is a possible alternative to temperature treatment. Soybean proteins play an important role in food consumption worldwide. The objectives of this study were to reveal the conformational changes of soybean β‐conglycinin after high pressure processing. The results will help the application of high pressure technology in soybean protein processing.
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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) | 0.001 | 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".