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Pharmacognosy Journal, 2013; 5(1):22-29
Research Article | doi:10.1016/j.phcgj.2012.12.004

Characterisation of leaf essential oils of three Cinnamomum species from Malaysia by gas chromatography and multivariate data analysis


Authors and affiliation (s):

Siti Y.M. Subkib, Jamia A. Jamala,*, Khairana Husain a, Nurhuda Manshoora

aDrug and Herbal Research Centre, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpur, Malaysia

bFaculty of Pharmacy, Universiti Teknologi Mara, Campus Puncak Alam, 42300 Bandar Puncak Alam, Selangor, Malaysia

Abstract:

Identification of chemical composition of essential oils as raw materials is very important for ensuring the quality of finished herbal products. The objectives of the study were to determine the chemical composition of essential oils of the leaves of three Cinnamomum species (i.e. Cinnamomum mollissimum Hook.f., Cinnamomum porrectum (Roxb.) Kosterm. and Cinnamomum verum J.S. Presl.) and to characterise the essential oils constituents by using GC and multivariate data analysis. Hydro-distilled essential oils were evaluated using GCeFID and GCeMS techniques. The GCeFID chromatograms were further analysed by multivariate data analysis using principal component analysis (PCA) and hierarchical clustering analysis (HCA) methods. The major compound identified in the oil of C. mollissimum was benzyl benzoate (77.69%), whereas that for C. porrectumwas safrole (93.19%) and C. verum was eugenol (93.08%). PCA score and HCA plots revealed that the leaf oils were classified into three separated clusters of C. verum (Cluster I), C. mollissimum (Cluster II) and C. porrectum (Cluster III) based on their characteristic chemical compositions. The combination of GC and multivariate data analysis may be used for identification and characterisation of essential oils from different Cinnamomum species that are to be used as raw materials of traditional herbal products.

Key words: Essential oils,Cinnamomum spp,Principal component analysis,Hierarchical cluster analysis.

 

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