Optimized Method to Analyze Rose Plant Volatile Organic Compounds by HS-SPME-GC-FID/MSD
摘要：A method involving Headspace solid-phase microextraction（HS-SPME） fiber combined with gas chromatography（GC） coupled with flame ionization detection（FID） and gas chromatography with mass spectrometry（GC-MS） was developed and optimized to investigate volatile organic compounds（VOCs） from different tissues（flowers, leaves, stems, rhizosphere and whole plants） of Floribunda and Hybrid Tea roses（intact and cut）. Three-phase fiber 50/30 μm divinylbenzene/carboxen/polydimethylsiloxane（DVB/CAR/PDMS） was used. Two types of chambers（Tedlar bag and glass jar） were evaluated for collection of VOCs and glass jar was selected. Absorbed compounds on the fiber were completely desorbed in the GC injector port at three desorption times（5, 10 and 15 min）, and 5 min at 250?C was used. The maximum extraction efficiency for flowers tissues（equilibrium absorption） was achieved 2 h after fiber exposure in the headspace for intact and cut Floribunda and Hybrid Tea flowers. Under the optimized HS-SPME and GC-FID/MS conditions, 1 h extraction time was chosen for intact and cut Floribunda and Hybrid Tea leaves and stems. The results demonstrated that 5 cm depth was selected for root and soil part（rhizosphere） for both rose cultivars, and 6 h and 12 h extraction time of VOCs from rhizosphere was achieved for Floribunda and Hybrid Tea, respectively. One hour was chosen for VOCs released from whole rose plants for both cultivars. In this study, the VOC profiles of two rose cultivars were characterized by the optimized HS-SPME-GC method. The different tissues of rose plants gave wide range of the VOCs; also the chromatograms of different cultivars were quite different and the specific VOC pattern of rose types depends on the species. Results from this study demonstrate the feasibility of this method for identifying VOCs from two rose cultivars and the potential use of this method for physiological studies on rose plants or on other floriculture plants.
Workshop 3 2017