MVM is so Important to High Resolution GC-MS
High Resolution Accurate Mass GC-MS is commonly used to identify significant, unknown compounds in all kinds of samples.
This kind of GC-MS work has different needs as far as sample preparation is concerned. When you don’t know the characteristics of the analytes you are looking for, your sample preparation must be non-selective so as not to discriminate against the compounds you are seeking to identify.
Contrast this with target analysis, where sample preparation selectivity in favour of your targets is usually a good thing.
When analysing for unknown volatile organics in dirty or complex matrices, you encounter a fundamental problem in choosing the most appropriate sampling method, since all of the current, widely used techniques have high degrees of discrimination against certain classes of compounds, dependent upon their volatility and functionality.
There is a new dynamic headspace sampling technique called the Multi-Volatile Method (MVM). MVM overcomes this weakness and offers a way preparing complex samples so that all compounds below a given volatility threshold can be injected into the GC-MS in a non-selective fashion.
In a recent post, I highlighted a great paper published in the Journal of Chromatography, that details some great work done by the GERSTEL applications team working in Tokyo, who have developed this technology. If you are working with high resolution GC-MS then this paper will be well worth taking the time to read:
The high resolution accurate mass system that Anatune promotes is the MultiFlex GC/Q-TOF. This combines terrific flexibility in automated sample prep, with the brilliant Agilent 7200 GC/Q-TOF.
The MultiFlex GC/Q-TOF supports automated MVM sampling capability to offer discrimination-free sampling of unknown volatile organics in complex samples.
If you would like to know more about this, or would like to see the MultiFlex GC/Q-TOF and MVM working in our Cambridge laboratory, please call us on +44 (0)1223 279210 or email: firstname.lastname@example.org