Seasonal Variations in the Composition of the Essential Oils of Rosemary (Rosmarinus officinalis, Lamiaceae)

Abstract

Seasonal variations in the composition of the essential oils obtained from rosemary plants of the same genotype cultivated in Belgrade were determined by GC and GC/MS. The main constituents were camphor (18.2-28.1%), 1,8-cineole (6.4-18.0%), α-pinene (9.7-13.5%), borneol (4.4-9.5%), camphene (5.1-8.7%), β-pinene (2.1-8.1%), β-phellandrene (4.6-6.5%), myrcene (3.4-5.9%) and bornyl acetate (0.2-7.9%). Cluster analysis showed that 16 samples that had been collected each month during the vegetative cycle can be separated into three main clades with different compositions of essential oils. In the shoots with fruits ('fruits'-Clade I) and shoots with developed leaves ('old shoots'-Clade III) camphor is dominant. In shoots with young and incompletely developed leaves ('young shoot'-Clade II) camphor and 1,8-cineole had almost the same concentration. The fact that the same genotype during the growing seasons can synthesize oils that are so different that they can be classified as different chemotypes confirms the opinion that the chemical composition of essential oils sometimes critically depends on the time of collection. Also, for the definition of chemotypes it is not enough to base this on a chemical analysis of an oil from one phenophase only.

Antioxidant Activity of Several Essential Oils from Different Rosmarinus officinalis Cultivars Grown in Sanremo (Italy)

“…The chemical composition of rosemary EOs was reported in many papers from the literature and it is well known that several factors influenced their composition [11][12][13][14]. As a consequence of this, the biological activity of rosemary EOs can change [3].…”

Phytochemical Study of Eight Medicinal Plants of the Lamiaceae Family Traditionally Used as Tea in the Sharri Mountains Region of the Balkans

“…ese results are in line with the published data on rosemary essential oil composition. Environmental, seasonal, and population-based variations in the chemical composition of rosemary essential oils have also been reported [36][37][38][39].…”

Multivariate statistical treatment of plant extract compositional data: Average mass scan of the total ion chromatogram (AMS) approach

“…Multivariate statistical analysis (MVA) has been repeatedly used to reveal evolutionary relationships among different plant species or track storage effects in the case of economically and/or pharmacologically important plant extracts/essential oils [1][2][3][4][5][6]. Two MVA analyses, most frequently employed in this sense, are the principal component analysis (PCA) and the agglomerative hierarchical cluster analysis (AHC) [1][2][3][4][5][6][7].…”

“…Multivariate statistical analysis (MVA) has been repeatedly used to reveal evolutionary relationships among different plant species or track storage effects in the case of economically and/or pharmacologically important plant extracts/essential oils [1][2][3][4][5][6]. Two MVA analyses, most frequently employed in this sense, are the principal component analysis (PCA) and the agglomerative hierarchical cluster analysis (AHC) [1][2][3][4][5][6][7]. The most usual (later on termed "traditional" or "classical") approach to MVA analyses of the plant derived mixtures utilizes percentages of single compounds or sometimes summed percentages of structurally and/or biogenetically related compounds from TIC or FID chromatograms as MVA variables, while the samples (botanical mixtures) represent the observations among which (dis)similarity is sought after [1][2][3][4][5][6][7].…”

“…Two MVA analyses, most frequently employed in this sense, are the principal component analysis (PCA) and the agglomerative hierarchical cluster analysis (AHC) [1][2][3][4][5][6][7]. The most usual (later on termed "traditional" or "classical") approach to MVA analyses of the plant derived mixtures utilizes percentages of single compounds or sometimes summed percentages of structurally and/or biogenetically related compounds from TIC or FID chromatograms as MVA variables, while the samples (botanical mixtures) represent the observations among which (dis)similarity is sought after [1][2][3][4][5][6][7]. However, the "classical" MVA-GC-FID/MS approach suffers from several shortcomings [1,7].…”