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Gcms postrun

Manufactured by Shimadzu
Sourced in Japan

The GCMS PostRun is a software component designed to be used in conjunction with Shimadzu's gas chromatography-mass spectrometry (GC-MS) systems. The core function of the GCMS PostRun is to enable the processing and analysis of data generated from GC-MS runs.

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3 protocols using gcms postrun

1

Metabolomics Data Processing Workflow

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Raw data was converted to “mzXML” format with the GCMS PostRun from Shimadzu company, and then converted to “abf” format with the ABF converter. The MS DIAL with Fiehn library were used for raw peaks exaction (files of “abf” format), the data baseline filtering and calibration of the baseline, peak alignment, deconvolution analysis, peak identification and integration of the peak height [42 (link)]. The example can be found in Figures S4 and S5, using metabolites lactulose. Average peak width of 20 scan and minimum peak height of 10,000 amplitudes was applied for peak detection, and sigma window value of 0.5, EI spectra cut-off of 5000 amplitudes was implemented for deconvolution. For identification setting, the retention time tolerance was 0.5 min, the m/z tolerance was 0.5 Da, the EI similarity cut-off was 70%, and the identification score cut-off was 70%. In the alignment parameters setting process, the retention time tolerance was 0.075 min, and retention time factor was 0.5.
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2

Distinguishing Saussurea tetrandra Origins via GC-MS and UHPLC-Q-TOF-MS/MS

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The GC-MS and UHPLC-Q-TOF-MS/MS data of S. tetrandra from different origins were exported in MZ format using the GC-MS Postrun (Shimadzu, Kyoto, Japan) and Agilent Masshunter Qualitative Analysis software packages, respectively. The peak finding, alignment, and filtering of the raw data were preprocessed using R 2.7.2 software (R Foundation for Statistical Computing, Vienna, Austria) to obtain the Rt, m/z, and peak strength of each compound. Finally, the obtained data were imported into Simca-P 14.1 (Umetrics, Umea, Sweden) for OPLS-DA. Potential chemical markers to differentiate the S. tetrandra from different origins were screened according to the VIP value. R2 and Q2 values were used to validate the model. R2 implied the explanation capability towards original data, and Q2 indicated the prediction ability of the model. The discriminant analysis function equation was established by SPSS 21.0 software.
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3

GC-MS Analysis of Organic Compounds

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GC–MS (gas chromatography–mass spectrometry) analysis was undertaken on a Shimadzu Nexis GCMS 2030 spectrometer equipped using a split/splitless injector and a separation column (SH-Rtx-5MS, 30 m × 0.25 mm, 0.25 µm film) or a fused silica capillary column (FAMEWAX, 30 m × 0.25 mm, 0.25 µm film). Helium (99.999%) (BOC, North Ryde, NSW, Australia) was used as the carrier gas at a flow rate of 1.5 mL/min. An aliquot (1.0 μL) of MEO in n-hexane (40 µg mL−1) was injected in the split mode at a 30:1 ratio, with the injector temperature being 250 °C. The temperature program was set initially at 60 °C for 1 min, increased to 260 °C at a rate of 10 °C/min, and held for 3 min. The ion source and transfer line temperatures were 230 °C and 250 °C, respectively. The ionization method was electron impact (70 eV). Spectral results were obtained over a mass range of m/z 35–600. RIs were calculated by analyzing the n-alkane series (C8–C40) with SH-Rtx-5MS or FAMEWAX under the same GC operating conditions. For the identification of chemicals, the mass spectra were analyzed using the Shimadzu GCMS Postrun and compared with authentic samples, if available, NIST library (NIST17-1, NIST17-2, NIST17s) search, mass fragmentation patterns, and RIs published in the literature.
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