Rapid discrimination and feature extraction of three Chamaecyparis species by static-HS/GC-MS

This study aimed to develop a rapid and accurate analytical method for discriminating three Chamaecyparis species (C. formosensis, C. obtusa, and C. obtusa var. formosana) that could not be easily distinguished by volatile compounds. A total of 23 leaf samples from three species were analyzed by static-headspace (static-HS) coupled with gas chromatography-mass spectrometry (GC-MS). The static-HS procedure, whose experimental parameters were properly optimized, yielded a high Pearson correlation-based similarity between essential oil and VOC composition (r = 0.555-0.999). Thirty-six major constituents were identified; along with the results of cluster analysis (CA), a large variation in contents among the three different species was observed. Principal component analysis (PCA) methods illustrated graphically the relationships between characteristic components and tree species. It was clearly demonstrated that the static-HS-based procedure enhanced greatly the speed of precise analysis of chemical fingerprint in small sample amounts, thus providing a fast and reliable tool for the prediction of constituent characteristics in essential oil, and also offering good opportunities for studying the role of these feature compounds in chemotaxonomy or ecophysiology.