Microemulsion as nanoreactor for lutein extraction: Optimization for ultrasound pretreatment
Bibliographic record
Abstract
In the present study, the capability of microemulsion technique, as a novel technique for synchronous extraction and solubilization of lipophilic compounds, on lutein extraction from marigold petals was investigated. Under the optimized sonication (amplitude 100%, 120 s, 25°C), the extraction efficiency increased (85%) using SDS:ethanol (1:2)-based ME. Moreover, sonication led to smaller droplets (12-163 nm) with favorable thermodynamic stability. In addition, the developed MEs showed higher thermal and especially UV stability in comparison to organic solvent extracts which were fainted with first-order kinetics. It was also found that co-surfactant could be eliminated from formulation on the expense of the optimized sonication, was valuable output form industrial point of view. These findings revealed the high potential of ultrasound technique on the extraction and solubilization of lutein by ME technique which can be directly utilized in lutein-enriched functional foods and beverages. PRACTICAL APPLICATIONS: From applicability point of view, the solvent extracted compounds cannot be easily dissolved in food or pharmaceutical systems that are mostly hydrophilic. Therefore, microemulsions (MEs), as green and environmentally friendly food-grade systems, due to their potential capability for simultaneous extraction and solubilization of carotenoids are of great interest. Therefore, the present study confirmed the practical ability of MEs in lutein extraction and protection. All in all, the developed lutein MEs with high lutein extraction capacity and superior lutein chemical stability against thermal treatment and especially UV radiation is an original finding which allows design of new functional foods and could be potentially useful for enriching foods, pharmaceuticals, nutraceuticals, and supplement formulation.
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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) | 0.000 | 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) | 0.000 | 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".