Secondary structure of proteins on oil release in aqueous enzymatic extraction of rapeseed oil as affected hydrolysis state
Bibliographic record
Abstract
In this study, the relationship between protein hydrolysis and oil release was studied by fluorescence microscopy and Fourier Transform Infrared (FTIR) spectroscopy, respectively. Five commercial proteases (Flavourzyme 1000L, Neutrase 1.5MG, Protamex, Alcalase 2.4L, and Thermolysin) were evaluated for oil extraction from rapeseed, and Alcalase 2.4L was found to be the best enzyme for highest free oil yield (81.81%). When use this enzyme, the results showed that parts of proteins restricted the release of oil, and the oil released almost completely was achieved after hydrolysis for 3.0 h. To further reveal the relationship between oil release and protein hydrolysis, the effects of hydrolysis time on the degree of hydrolysis of protein, and free oil yield were studied. The results showed that the release of oil was only related to the hydrolysis of parts of proteins, and when the degree of hydrolysis of protein was more than 17.05%, the effect of protein hydrolysis on oil release was greatly reduced. The intrinsic relationship between the structural changes of proteins and the release of oil in the hydrolysis process was analyzed by comparing protein secondary structure of different hydrolysis time, and the results showed that the transition of protein structure from order to disorder was an important factor for oil release.
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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".