Abstract
Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.
Highlights
Is, if not the base peak, a major ion common to all TMS derivatives. This manuscript presents a suite of scripts developed for GC×GC-TOFMS metabolomics data with the aim of rapid screening of complex biological samples, which typically contain thousands of compounds, comprised of diverse compound classes
The scripts presented were written using only mass spectral information. This provides a significant advantage because these scripts are independent of separation parameters and can be applied to any GC or GC×GC-TOFMS chromatograms, regardless of the column combinations and GC and MS conditions used
Thesacrifice addition of retention time accuracy levels of the for scripts further.based. Sinceon this would significantly their versatility, we information may increase the accuracy of the scripts further
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. GC×GC techniques provide chromatograms with an inherently ordered structure, which is useful for the identification of unknown compounds This technique is advantageous through the possibility of “seeing everything”; the TOFMS allows the capture of complete mass spectra at every point [5]. It is common that a detected analyte is not registered in the mass spectral library database This is especially true for trimethylsilyl (TMS) derivatives of compounds, generated with a gold standard derivatization method for metabolomics samples [14]. Is, if not the base peak, a major ion common to all TMS derivatives This manuscript presents a suite of scripts developed for GC×GC-TOFMS metabolomics data with the aim of rapid screening of complex biological samples, which typically contain thousands of compounds, comprised of diverse compound classes. To the best of our knowledge, this represents the first collection of automated filtering scripts for handling GC×GC-TOFMS data in metabolomics applications
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