Physicochemical properties, antioxidant stability and micronutrients preservation analyses of rice bran oil by joint acid and multi-enzymes degumming
Bibliographic record
Abstract
Degumming, a critical process in the edible oil industry, is essential for removing phospholipids . Traditional methods such as water-degumming and acid-degumming have limitations in this regard. This study aimed to assess the efficacy of different degumming techniques, including acid, single and multi-enzyme methods, on rice bran oil (RBO). The investigation also focused on the impact of these techniques on the physicochemical characteristics and preservation of micronutrients in RBO during the degumming process. The primary phospholipids identified were phosphatidylethanolamine (29.51 %), phosphatidylcholine (37.00 %), and phosphatidylinositol (24.49 %). Acid degumming removed 84.23 % of phospholipids , while a significantly higher removal rate of 98.7 % was achieved with the combination of phospholipase A1 & phospholipase C. The degumming process effectively inhibited oxidation in RBO, leading to a substantial increase in the oxidation induction time from 5.7 to 10.0 hours. Furthermore, multi-enzyme degumming showed slightly greater radical-scavenging activity compared to single enzyme degumming in RBO. However, the levels of micronutrients such as phenols, sterols, tocopherols , squalene, and oryzanol were reduced by 6.27–22.17 %. This study provides a comprehensive analysis of the effects of different degumming processes on the physicochemical properties , fatty acid profiles, antioxidant capacities , and preservation of micronutrients in RBO.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".