Briefly, I conducted an experiment on the potential emission of characteristic organic volatile compounds by plants growing on soil in which animal tissues were buried.
The resulting data can be analyzed, for example, in Qualitative Agilent Masshunter. However, in order to compare the data, looking at the chromatograms with the naked eye is insufficient. I think you should first analyze the raw data (retention time and intensity) to find evnentual trends, clues where such a signal is stronger and whether it is related to a specific chemical compound.
I'm adding two example files - one with raw data, and the other with pre-processed data (about 13,000 retention time values were divided into 20 intervals, and each interval has a summed signal).
Hello Cholewa,
Well the RT is used to understand the particular compound that has been under investigation against standard.
The integration of peaks is done to get the compound identification in Library search for double varification.
Now to calculate the concentration you can use the standard solution concentration and your test compound concentration you have taken.
By applying the mathematical equations in Excel.
Retention times are dependent on polarity of the GC column's packing. Capillary columns are much better. But GC -mass spec then becomes critical.....and your computer program does the matching for you.
Dear Marcin,
Definitely, eye comparison is not enough. This is why Agilent has MassHunter which is in my opinion a very food software, particularly for GC-MS. MassHunater will extract peaks and integrate them to provide you numerical data for the downstream analysis.
MassHunater is quite intuitive, but maybe you need some assistance to start using it. So, I would recommend you to find somebody locally who knows how to use it, and with this help you will be able to get some initial results the same day. Please keep in mind that there are many important features in the software, but the simplest analysis can be done without them (of course it will sacrifice the quality of the analysis, but it still will be better than nothing).
Your goal will be a spread-sheet with a combination of identified (if you have a build-in library) and unidentified metabolites (with their RT and m/z fragments), and their relative abundances. With these data you then can apply variety of statistical tools to make the data meaningful.
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