Plant Extracts Containing Saponins Affects the Stability and Biological Activity of Hempseed Oil Emulsion System
Bibliographic record
Abstract
In this study, two saponins-rich plant extracts, viz. Saponaria officinalis and Quillaja saponaria, were used as surfactants in an oil-in-water (O/W) emulsion based on hempseed oil (HSO). This study focused on a low oil phase content of 2% v/v HSO to investigate stable emulsion systems under minimum oil phase conditions. Emulsion stability was characterized by the emulsification index (EI), centrifugation tests, droplet size distribution as well as microscopic imaging. The smallest droplets recorded by dynamic light scattering (droplets size v. number), one day after the preparation of the emulsion, were around 50–120 nm depending the on use of Saponaria and Quillaja as a surfactant and corresponding to critical micelle concentration (CMC) in the range 0–2 g/L. The surface and interfacial tension of the emulsion components were studied as well. The effect of emulsions on environmental bacteria strains was also investigated. It was observed that emulsions with Saponaria officinalis extract exhibited slight toxic activity (the cell metabolic activity reduced to 80%), in contrast to Quillaja emulsion, which induced Pseudomonas fluorescens ATCC 17400 growth. The highest-stability samples were those with doubled CMC concentration. The presented results demonstrate a possible use of oil emulsions based on plant extract rich in saponins for the food industry, biomedical and cosmetics applications, and nanoemulsion preparations.
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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) | 0.000 | 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) | 0.001 | 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".