Optimization of subcritical water extraction of phenolic compounds from Ziziphus jujuba using response surface methodology: evaluation of thermal stability and antioxidant activity
Bibliographic record
Abstract
Abstract Background The jujube is mainly grown in the subtropical and tropical regions of Asia. Due to owning bioactive compounds such as polyphenols, it was considered as medicinal and nutritional plant in traditional medicine. This study aimed to extract phenolic compounds from Ziziphus jujuba using subcritical water (SCW) process. The possible combinations of temperature, time, and fruit-to-solvent ratio were investigated using response surface methodology. Results The total phenolic compounds (TPC) and radical scavenging capacity (RSC) of 975.94 mg/g and 53.98%, respectively, were recovered at optimum extraction conditions (170 °C, 74.49 min, and fruit-to-solvent ratio of 1:5.29). The extract obtained in SCW optimum conditions was put under thermal treatments including low temperature long time, high temperature short time, cooking, baking, and sterilization. The results showed after baking, the amount of TPC, RSC, and absorbance at 420 nm increased. The quantity of gallic acid, chlorogenic acid, p -coumaric acid, ferulic acid, and rutin increased significantly. The efficiency of SCW extract in retarding lipid oxidation in bulk oil and O/W emulsion model was investigated at 50 °C for 10 days. After 10 days, the peroxide value of bulk oil (1.1 meq O 2 /kg) containing SCW extract was lower than O/W emulsion model (2.2 meq O 2 /kg). Conclusion The subcritical extracted phenolic compounds seem to have good antioxidant activity as well as thermal stability for using in food or drug industries.
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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) | 0.001 | 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".