The effect of cyclodextrin complexation on the solubility and photostability of nerolidol as pure compound and as main constituent of cabreuva essential oil
Bibliographic record
Abstract
Nerolidol (Ner), a major component of many plant essential oils, is known for its various biological properties. However, the low solubility of Ner in water and its susceptibility to degradation limit its application. The aim of our study was to improve the solubility and photostability of Ner through its encapsulation in different cyclodextrins (CDs). The formation constants of cis -, trans -Ner and their commercial mixture with various CDs (α-CD, β-CD, γ-CD, HP-β-CD, RAMEB, CRYSMEB and SBE-β-CD) were determined by phase solubility studies and confirmed by the spectral displacement UV-visible method. The solubility of cabreuva essential oil (EO) rich in trans -Ner was also evaluated by total organic carbon (TOC) analysis. The encapsulation efficiency (EE %) of Ner in HP-β-CD solid complexes was assessed by HPLC. The structural characterization of CD/ trans -Ner inclusion complex was then conducted by NMR spectroscopy followed by molecular modelling studies. The effect of encapsulation on the Ner photostability was also carried out over time under UVB irradiation. A L -type phase-solubility diagrams were obtained, suggesting the formation of 1:1 CD/Ner inclusion complexes. The solubility of Ner was enhanced by approximately 70-fold in the presence of 10 mM HP-β-CD. Moreover, high EE % values were obtained for 5:1 and 10:1 HP-β-CD:Ner molar ratios. NMR and molecular modelling studies revealed the most stable structure for trans -Ner inside the CD cavity with the OH group oriented towards the wider rim of the CD. Finally, CD encapsulation of Ner as pure compound or as main component of the cabreuva EO, protected it from degradation. This effect was more pronounced as the concentration of CD increased. These findings suggested that CDs are promising encapsulating carriers for Ner by enhancing its solubility and stability and thereby its application in food industry.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".