Assessment of variability of essential oil components in different accessions of Damask rose (Rosa damascena Mill.) by multivariate analysis

Trends in Phytochemical Research

مقاله 4، دوره 5، شماره 4، اسفند 2021، صفحه 199-208   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/tpr.2021.1940496.1222

نویسندگان

Borzou Yousefi^{* 1}؛ Khosrow Shahbazi²؛ Hassan Khamisabady³؛ Mohamad Gheitury³

¹Kermanshah Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), P.O. Box 67145-1661, Tel: [phone], Kermanshah, Iran

²Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

³Kermanshah Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran

چکیده

In this work, 22 Damask rose accessions were cultivated in a randomized complete block design with 3 replications. Then, chemical components of essential oils (EOs) were identified and subsequently characterized using gas chromatography (GC) and gas chromatography mass spectrometry (GC/MS) techniques. Accordingly, sixteen chemical compounds were identified, which accounted for about 85.3-99.8% of the EOs. Accessions were classified in 4 groups by cluster analysis. Discriminant function analysis (DFA) was found to be in line with the results of cluster analysis approach. In addition, using principal component analysis (PCA), the first 5 components had Eigen values higher than 1. n-Eicosane, n-heneicosane, n-tricosane, n-nondecane and n-tetradecanal had maximum portions in first component, while citronellyl acetate, geranial and n-undecanol had the maximum portions in the second component. Accessions of Isfahan5, Kermanshah5 and Kermanshah6 had the maximum percent of geraniol and citronellol, the two key chemical components, responsible for EO quality.

کلیدواژه‌ها

Cluster analysis؛ Damask rose؛ Discriminant function analysis (DFA)؛ Essential oil؛ GC/MS؛ Principal components analysis (PCA)

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