12 February / March 2016
spectra or containing very few ions being deconvolved. Any data system will process large, sharp peaks with a good signal to noise better than working on peaks present in the noise region. Reducing noise and improving signal can be applied throughout the GC-MS system from carrier gas to MS detector and sample preparation. Having some noise is good for the deconvolution software to be able to define the noise and therefore remove it in the process of producing the deconvoluted mass spectra. It can also means that peaks can be larger, as the base of them is deconvoluted from the noise region rather than the potential loss of the bottom part of the peak through using incorrect threshold values in the acquisition method.
Figure 5: AMDIS deconvolution showing TIC (white), EICs (coloured), non-deconvoluted TIC plus two deconvoluted mass spectra library hits [2].
Figure 6: (a) non-deconvoluted apex spectrum and all EICs for ions present, (b) ions with a different apex eliminated, (c) ions with different shape eliminated producing the deconvoluted spectrum.
Figure 7: Deconvolution and identification of SVOCs in an environmental sample [4].
solve identification problems due to the low mass being too high or the high mass being too low. Optimise the number of data points across the peak through considering the baseline peak width and acquisition rate, generally a minimum of 10 scans is required to improve the peak shape and
enable accurate identification of the apex, resulting in the ability to deconvolute closely co-eluting peaks. But too many data points (working to a maximum of 30 is optimal) can result in excess noise, this can lead to false apexes in the peak shape and additional peaks with the same mass
So, when and which deconvolution software should be used? The decision to employ deconvolution when identifying unknown or target analytes in complex sample is easy, particularly where it is impossible to obtain a high quality mass spectrum by background subtraction and result in a good library match. From semi-volatile organic compounds (SVOCs) in environmental samples (Figure 7) to chemical warfare agents in diesel oil (Figure 8) to aroma profiling in cheddar cheese (Figure 9), deconvolution enables the extraction of library searchable mass spectra. However, even an experienced chromatographer can be caught out by a matrix interferent in a relatively simple analysis resulting in the mass spectrum not making sense. Or we know an analyte is present in a SIM method but the ion ratios for what we thought were unique ions are erroneous. Performing library searches through huge databases is easy today, but does the result really match the application, is the match quality good enough, are there ions that look out of place? Deconvolution software comes in several forms and the best solution depends on the way you work along with your other analytical requirements. Some software packages involve set-up and optimisation of the parameters but allow control over what is found, others have no parameters and just work. Several options are offered from being embedded in the data analysis software, or as an add-on to the library, as part of the database software or even as an independent standalone piece of software, most enable you to ‘try before you buy’. Having deconvolution software that has been evaluated, tested and learned to trust is useful for any mass spectrometrist. Deconvolution is another tool that can add more power to your future methods but can also be used on data that you have already acquired.
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