SE134:/DS1

Sample Set Information

ID	SE134
Title	Unbiased profiling of volatile organic compounds in the headspace of Allium plants using an in-tube extraction device.
Description	Plants produce and emit important volatile organic compounds (VOCs), which have an essential role in biotic and abiotic stress responses and in plant–plant and plant–insect interactions. In order to study the bouquets from plants qualitatively and quantitatively, a comprehensive, analytical method yielding reproducible results is required. We applied in-tube extraction (ITEX) and solid-phase microextraction (SPME) for studying the emissions of Allium plants. The collected HS samples were analyzed by gas chromatography–time-of-flight–mass spectrometry (GC-TOF–MS), and the results were subjected to multivariate analysis. In case of ITEX-method Allium cultivars released more than 300 VOCs, out of which we provisionally identified 50 volatiles. We also used the VOC profiles of Allium samples to discriminate among groups of A. fistulosum, A. chinense (rakkyo), and A. tuberosum (Oriental garlic). As we found 12 metabolite peaks including dipropyl disulphide with significant changes in A. chinense and A. tuberosum when compared to the control cultivar, these metabolite peaks can be used for chemotaxonomic classification of A. chinense, tuberosum, and A. fistulosum.
Authors	Kusano M, Kobayashi M, Iizuka Y, Fukushima A, Saito K.
Reference	BMC Res Notes. 2016 Feb 29;9:133. doi: 10.1186/s13104-016-1942-5.
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PubMed

Data Analysis Details Information

ID	DS1
Title	Data analysis
Description	Data analysis Raw data were exported in the network common data form (NetCDF) file format using LECO ChromaTOF software (version 2.32) and then processed with the hierarchical multi-curve resolution (H-MCR) method. We obtained the normalized response for calculating the signal intensity of each metabolite from the mass-detector response by using the cross-contribution compensating multiple standard normalization (CCMN) method. The resolved mass spectra were matched against reference mass spectra in the NIST-L (version NIST05) using NIST MS search program (version 2.0, http://www.chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:ms-search). Peaks were tentatively identified according to the guidelines for metabolite identification. When mass spectra exhibited a match value greater than 799 and the corresponding peaks had RIs with small differences upon comparison of their resolved mass spectra and RIs against those in the reference libraries (Ad-L, 3rd and 4th edition, and Te-L) and against Vo and NIST-L (see Table 2 and “Results and discussion” section), the peaks were considered to be putatively annotated compounds. We compared the RIs of sulfur-containing metabolites and compounds we detected with those reported in the literature. To estimate the number of compounds that overlapped with each reference library, we first exported the mass spectral information, including the compound name, RI, synonyms, and m/z value, with relative peak intensity (maximum, 999; minimum, one) from each library in ASCII text format (.MSP) automatically. Then we compared the similarity of the mass spectra in each library using MassBank. The similarity (≥850 or 900) and the RI difference (<|30 unit|) were used to extract the same or very similar compounds from the query library and NIST05. It should be noted that the standard deviation (SD) of the absolute RI difference of these compounds is less than 8.8 when we applied similarity of ≥850.
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