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Column Type

Good day everyone, I joined the group not long ago and am very interested in Mass Spectrometry for use in Metabolomics.
Right now I am having a problem in terms of deciding which column to buy? I want to choose a very general use type of column, since I want to start with untargeted then move to targeted:

My choice of columns for our (UPLC) LC-MS/MS are:

1.) Kinetex Phenomenex
    C18 (size 150mm x 2.1)

2.) Shim-pack XR-ODS III
    composed of spherical silica particles, the surfaces of which are coated with octadecylsilyl (ODS) groups
    (size 150mm x 2.0)

Thank you very much for your time

Column Type

Reply #1 –
Hello,
According to my experience Phenomenex columns are better but Waters columns are best. Shimpack is also good but sometimes you can not get what you want. I recommend you to buy Phenomenex.

Column Type

Reply #2 –
Thank you very much, that was a great help!
I went ahead and bought the Phenomenex column.

Column Type

Reply #3 –
Here i have a query regarding GC-MS metabolomics data processing. Normally after deconvolution by AMDIS  each single peak consists different metabolites. These metabolites can be integrated by extracted ion approach by keeping specific m/z. Finally the data yields peak table consists of retention time, m/z in columns and samples in rows. Even though the procedure is specific and reproducible, but it is time taken. Especially for large samples like more than 100 samples, the procedure is too tedious. My query is that, is there any software tools available for retention time alignment and integration of extracted ions. I have used XC-MS for Rt alignment and integration by uploading GC-MS raw files (XCalibur) but it is giving numerous extracted ions for a single peak. By this procedure i have got 9474 specific extracted ions for a single sample. So other than XC-MS, is there any software tool available for making peak table from raw data after processing ( Rt alignment, noise removal etc) ????. If you know the information, kindly help me in solving the GC-MS data.