Effect of Thermal and High Electric Fields on Secondary Structure of Peanut Protein
Bibliographic record
Abstract
This study evaluated the effect of the thermal and high electric field stresses on the secondary structure conformation of peanut protein using Fourier transform infrared spectroscopy. The amide I region between the wavelengths 1700–1600 cm–1 of the spectra were studied for different thermal and high electric field treatments. Within thermal treatments, both hot air roasting and microwave processing treatments were evaluated. Hot air treatments were performed at temperatures of 50, 75, and 100°C from 15, 30, and 45 min while the microwave treatments were conducted at the same temperatures, but for 5, 10, 15, and 20 min. Three experimental conditions were evaluated for the electric field intensity of 10, 15, and 20 kV for 60, 120, and 180 min. Changes were observed at 1654–1650 cm–1, indicating conformational changes in the α-helix secondary structure. Similar changes were observed at various other wavelengths indicating changes in the 3/10 helix, β-sheets and random coils present in the protein. With an increase in the treatment time, the secondary structure reorganizations increased with the creation of new random coils and aggregated strands. Curve-fitting using Gaussian band shapes further supported the observations. In vitro protein digestibility studies were also performed and the protein changes also supported the observations from the spectra.
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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".