Gc ms postrun analysis

The profile of the essential oils investigated was assessed using the GC-MS technique according to the protocol [78 (link)]. Identification of all volatile constituents was based on comparison of experimentally obtained compound’s mass spectra with mass spectra available in the NIST20 database. Additionally, the retention indices (RI) obtained experimentally, calculated using macro [79 (link)], were compared with the RI available in the NIST20 database and the data from the literature [80 ]. Shimadzu software GCMS Postrun Analysis (Shimadzu Company, Kyoto, Japan) and ACD/Spectrus Processor (Advanced Chemistry Development, Inc., Toronto, ON, Canada) were used to process the data. The quantification of identified constituents was performed by calculation based on the amount of added internal standard and expressed as a percentage of integrated peaks’ area. Analysis was performed using the Shimadzu 2020 apparatus (Varian, Walnut Creek, CA, USA) equipped with a Zebron ZB-5 MSI (30 m × 0.25 mm × 0.25 μm) column (Phenomenex, Torrance, CA, USA). The temperature of the GC oven was programmed from 50 °C to 250 °C at a rate of 3.0 °C and kept for 3 min. Scanning was performed from 35 to 550 m/z in electronic impact (EI) at 70 eV and ion source temperature 250 °C. Samples were injected at split ratio 1:10 and gas helium was used as the carrier gas at a flow rate of 1.0 mL/min.

Raw LC-MS data were converted tomzXML format using MSconvert tools (Version 3,64 bit, proteowizard, Palo Alto, CA, USA). Raw GC-MS data were converted to mzXML format using GC-MS Postrun analysis (Shimadzu, Kyoto, Japan). Preprocessing of MS data including peak picking, peak grouping, retention time (RT) correction, and integration was performed using the XCMS implemented with R software (Version 3.5, University of Auckland, New Zealand). Each ion was identified by the RT and m/z data. Intensities of each peak were recorded and a three-dimensional matrix containing arbitrarily assigned peak indices and ion intensity information was generated. The intensities of each ions identified were normalized and the quantitative data were analyzed by several unsupervised methods and supervised methods in R. PCA (principle components analysis) was used for multivariate exploration of clusters and trends among the observation. Feature selection were performed by Boruta package in R.

Metabolites were identified using an automated mass spectral deconvolution and identification system (AMDIS, NIST, USA) for spectral peak deconvolution and the National Institute of Standards and Technologies Mass Spectral library. A quantitative table containing the characteristic ions and the retention times of metabolites was imported to GC–MS Post run Analysis (Shimadzu, Japan) for batch processing of all samples. Identified metabolites with relative standard deviations < 10% in QC samples were used for further analyses. Multivariate analysis was conducted in SIMCA-P 11.5 (Umetrics, Sweden).