科研之友
微信
新浪微博
Facebook
分享链接
摘要
Introduction Volatile organic compounds (VOCs) occurring in leaves of plants carry information about the physiological state of the plant. Monitoring of VOCs assists in detecting plant stress before visible signs are present. Objective To establish and apply a simple workflow for the automated extraction, measurement and annotation/identification of Vitis vinifera cv. Pinot Noir leaf metabolites. Methodology Leaf samples were harvested, cooled with liquid nitrogen and homogenised under cooled conditions. VOCs were extracted and enriched by solid phase microextraction (SPME) and analysed by GC-MS. Samples were measured on two columns with different polarity of stationary phases. Mass spectral deconvolution and identification was done by AMDIS software. Strict identification criteria were applied: match factor = 90; relative retention index deviation = 2% from reference value on both columns. Data of two sampling dates were analysed with multivariate statistics. Results We found similar to 600 components in a single chromatogram. Applying the mentioned criteria resulted in annotation of 63 metabolites of which 47 were confirmed with authentic standards. For the majority of the compounds technical variability was < < 40% (RSD), biological variability among plants was 7-119%. Principal component analysis (PCA) scores plot of leaf samples from two different sampling dates showed two clearly separated clusters. The presented workflow enabled for the first time the detection and identification of 19 metabolites that have so far not been described for Vitis spp. Conclusion The developed workflow enabled the identification of grapevine leaf metabolites, which allowed the separation of leaves from two sampling dates by PCA.
- 出版日期2012-8
