Chemical variability of Cleistopholis patens (Benth.) Engl. et Diels leaf oil from ivory coast.
The chemical composition of 48 leaf oil samples isolated from individual plants of Cleistopholis patens (Benth.) Engl. et Diels harvested in four Ivoirian forests was investigated by GC-FID (determination of retention indices), GC/MS, and (13) C-NMR analyses. The main components identified were β-pinene (traces-59.1%), sabinene (traces-54.2%), (E)-β-caryophyllene (0.3-39.3%), linalool (0.1-38.5%), (E)-β-ocimene (0.1-33.2%), germacrene D (0.0-33.1%), α-pinene (0.1-32.3%), and germacrene B (0-21.2%). The 48 oil compositions were submitted to hierarchical clustering and principal components analyses, which allowed the distinction of three groups within the oil samples. The oil composition of the major group (GroupI, 33 samples) was dominated by (E)-β-caryophyllene and linalool. The oils of Group II (eight samples) contained mainly β-pinene and α-pinene, while those of Group III (seven samples) were dominated by sabinene, limonene, and β-phellandrene. Moreover, the compositions of the Ivoirian C. patens leaf oils differed from those of Nigerian and Cameroonian origins.
Université de Corse-CNRS, UMR 6134 SPE, Equipe Chimie et Biomasse, Route des Sanguinaires, F-20000 Ajaccio (phone: +33-4-95524123; fax:+33-4-95524142); Laboratoire de Chimie Organique Biologique, UFR-SSMT, Université Félix Houphouët-Boigny, BPV 34 Abidjan, Côte d'Ivoire.
Zana Adama
ZA
Ouattara
Jean Brice
JB
Boti
Koffi Barthelemy
KB
Attioua
Antoine Coffy
AC
Ahibo
Joseph
J
Casanova
Félix
F
Tomi
Ange
A
Bighelli
D029061:Annonaceae / Q000737:chemistry*; D016000:Cluster Analysis; D007560:Cote d'Ivoire; D008401:Gas Chromatography-Mass Spectrometry; D009682:Magnetic Resonance Spectroscopy; D009822:Oils, Volatile / Q000032:analysis*; D018515:Plant Leaves / Q000737:chemistry*; D010938:Plant Oils / Q000032:analysis*; D025341:Principal Component Analysis
D009822:Oils, Volatile; D010938:Plant Oils
Cleistopholis patens; Essential oils; Hierarchical cluster analysis (HCA); Principal-component analysis (PCA)