A headspace SPME GC-TOF-MS method was developed for the acquisition of metabolite profiles of apples volatiles. As a first step, an experimental design was applied to find out the most appropriate conditions for extraction of apple volatile compounds by SPME. The selected SPME method was applied in profiling of four different apple varieties by GC-EI-TOF-MS. Full scan GC-MS data were processed by MarkerLynx software for peak picking, normalisation, alignment and feature extraction. Advanced chemometric/statistical techniques (PCA and PLS-DA) were used to explore data and extract useful information. Characteristic markers of each variety were successively identified using the NIST library thus providing useful information for variety classification. The developed HS-SPME sampling method is fully automated and proved useful in obtaining the fingerprint of the volatile content of the fruit. The described analytical protocol can aid in further studies of the apple metabolome.
Aprea, E.; Gika, H.; Carlin, S.; Theodoridis, G.; Vrhovsek, U.; Mattivi, F. (2011). Metabolite profiling on apple volatiles content based on solid phase microextraction and gas-chromatography time of flight mass spectrometry. JOURNAL OF CHROMATOGRAPHY A, 1218 (28): 4517-4524. doi: 10.1016/j.chroma.2011.05.019 handle: http://hdl.handle.net/10449/20057
Metabolite profiling on apple volatiles content based on solid phase microextraction and gas-chromatography time of flight mass spectrometry
Aprea, Eugenio;Carlin, Silvia;Vrhovsek, Urska;Mattivi, Fulvio
2011-01-01
Abstract
A headspace SPME GC-TOF-MS method was developed for the acquisition of metabolite profiles of apples volatiles. As a first step, an experimental design was applied to find out the most appropriate conditions for extraction of apple volatile compounds by SPME. The selected SPME method was applied in profiling of four different apple varieties by GC-EI-TOF-MS. Full scan GC-MS data were processed by MarkerLynx software for peak picking, normalisation, alignment and feature extraction. Advanced chemometric/statistical techniques (PCA and PLS-DA) were used to explore data and extract useful information. Characteristic markers of each variety were successively identified using the NIST library thus providing useful information for variety classification. The developed HS-SPME sampling method is fully automated and proved useful in obtaining the fingerprint of the volatile content of the fruit. The described analytical protocol can aid in further studies of the apple metabolome.File | Dimensione | Formato | |
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