Effect of sesame paste by protease hydrolysis: Physicochemical properties, storage stability, and flavor
Bibliographic record
Abstract
Abstract In this study, the effect of protease hydrolysis on the stability and flavor of a sesame oil–paste system was investigated. The optimum amount of protease addition, determined by testing the effects of protease addition on the improvement in the oil–paste separation of sesame paste (SP), was investigated using 7% neutral protease (NP), 5% papain (PP), 7% trypsin (TP), and 5% flavourzyme (FZ). The flavor differences among these four groups of samples were investigated, and storage experiments were conducted for 28 days to observe the changes in quality. Finally, the principal component analysis (PCA) calculations showed that the samples in the 5% FP group performed the best, with a considerable improvement in the stability of the sesame paste–oil system during storage. The oil separation capacity (OSC) decreased by 9.1% during storage, the acid value (AV) increased by 1.00 mg/g and the peroxide value (POV) increased by 0.3 mmol/kg compared with those of the control group. This group also had the highest total sensory (4.25 score) and nutty (5.83 score) scores based on the total pyrazine content. Therefore, protease hydrolysis has promising application prospects for increasing the stability of sesame paste.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".