Variation in Essential Oil Gland Density, Yield and Composition of Aerial Parts of Eucalyptus tereticornis Sm. and Eucalyptus camaldulensis Dehnh

Essential oils (EOs) isolated from aerial parts (leaves, buds and twigs) of Eucalyptus tereticornis Sm. and Eucalyptus camaldulensis Dehnh were analysed for yield, specific gravity (SG), refractive index (RI) and chemical composition. Chemical composition analysis of the EOs was carried out by GC/MS technique. The transverse section (TS) of the leaf was subjected to oil glands measurement by Stereoscopic Zoom Microscope. EOs yield from aerial parts of E. tereticornis varies from 1.0-2.0 % (10-20 g kg(-1) leaves biomass), 0.7-1.5 % (7-15 g kg(-1) buds biomass) and 0.3-0.5 % (3-5 g kg(-1) twigs biomass), while in E. camaldulensis 0.8-1.4 % (8-14 g kg(-1) leaves biomass), 1.0-1.4 % (10-14 g kg(-1) buds biomass) and 0.4-0.7 % (4-7g kg(-1) twigs biomass). RI and SG values for EOs from E. tereticornis were observed in the ranges of 1.442-1.479 and 0.883-0.913 respectively. Similarly, RI and SG values for EOs from E. camaldulensis were observed in the ranges of 1.44-1.462 and 0.889-0.993 respectively. Chemical composition analysis confirms that E. tereticornis oil was a cineole type (1,8-cineole: 21.31-29.15 %) whereas E. camaldulensis terpinene type (y-terpinene: 22.33-26.91 %). Average oil gland density in leaf of E. tereticornis and E. camaldulensis was observed as 2.7x10 7 n/m(2) and 2.5x10(7) n/m(2) respectively. Cluster analysis differentiates both the eucalyptus species in terms of EO composition. In addition, four and five principal components can be extracted for E. tereticornis and E. camaldulensis respectively and which explains 96.26 % and 97.87 % of the accumulated variation of the data analysed.

Automated summary

This study analyzed the essential oils isolated from aerial parts of Eucalyptus tereticornis and Eucalyptus camaldulensis, revealing variations in yield and chemical composition. Cluster analysis differentiated the two eucalyptus species based on EO composition, while principal component analysis explained a high percentage of the data variability for each species.