The effect of various extraction techniques on the quality of sage (Salvia officinalis L.) essential oil, expressed by chemical composition, thermal properties and biological activity

•Sage essential oils was isolated by different extraction techniques.•Principal compound was viridiflorol followed by camphor, thujenes, and verticiol.•Samples were assessed for antioxidant, antimicrobial, and cytotoxic activities.•Obtained essential oils were investigated for thermal behavior by TG...

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Veröffentlicht in:Food Chemistry: X 2022-03, Vol.13, p.100213-100213, Article 100213
Hauptverfasser: Đurović, Saša, Micić, Darko, Pezo, Lato, Radić, Danka, Bazarnova, Julia G., Smyatskaya, Yulia A., Blagojević, Stevan
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Sprache:eng
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Zusammenfassung:•Sage essential oils was isolated by different extraction techniques.•Principal compound was viridiflorol followed by camphor, thujenes, and verticiol.•Samples were assessed for antioxidant, antimicrobial, and cytotoxic activities.•Obtained essential oils were investigated for thermal behavior by TGA analysis.•ANN model was developed, for the anticipation of antioxidant activity. In this study, influence of the extraction techniques on the quality of the sage essential oil was investigated. Obtained samples were analyzed for chemical composition by GC/MS, thermal properties by thermogravimetric analysis (TGA), and for biological activity: antioxidant (DPPH, CUPRAC, FRAP, ABTS, HRSA and TBARS), microbiological (Staphylococcus aureus, Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa, Candida albicans, and Aspergillus niger), and cytotoxic (HeLa, LS-174, A549 and MRC-5) activities. Chemical composition showed that viridiflorol was principal compound in all samples followed by camphor, thujones, and verticiol. MWD 400 W was the most potent antioxidant agent, D 200 W and MWD 400 W antimicrobial agents, while hydrodistallates (D 200 W and D 400 W) were the most potent cytotoxic agents. An artificial neural network model was developed for the antioxidant activity anticipation of analyzed samples. These models showed good prediction properties (the r2 value during training cycle for output variables was 0.998).
ISSN:2590-1575
2590-1575
DOI:10.1016/j.fochx.2022.100213