A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software

Abstract

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

Top-Down Approach to Retention Time Prediction in Comprehensive Two-Dimensional Gas Chromatography–Mass Spectrometry

“…Typically, cryogenically modulated peaks present peak widths ranging between 0.1 and 0.2 s. In this regard, the modeling errors could be estimated to 2–3 peak widths. Additionally, the comparison of multiple commercially available GC × GC software highlighted the presence of an average difference of 0.15 s between the reported 2 t r when changing software due to differences in the data transformation algorithms . In light of this comparison, the errors yielded by the present approach do not seem outrageous but rather lie in quite a reasonable range.…”

Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis

“…Recently, more open-source data sets have become available; for instance, Focant and co-workers have provided two data sets, consisting of either five fruit beers 32 or nine flavored chocolate samples, 143 that can be used to benchmark the performance of nontargeted methods. Using the chocolate data set, Weggler et al demonstrated that both the number of peaks aligned in the final peak tables and clustering on the PCA scores plot differed among eight commercial software packages for GC×GC data analysis.…”

“…Using the chocolate data set, Weggler et al demonstrated that both the number of peaks aligned in the final peak tables and clustering on the PCA scores plot differed among eight commercial software packages for GC×GC data analysis. 143 Because it is difficult to ascertain the differences in proprietary vendor software, there has been increased interest in developing free and open-source software (FOSS). 144−147 With FOSS, the user can see exactly how the software treats their data and make changes to the software if necessary.…”

“…Hence, access to standardized, publicly available data sets can provide researchers the means of validating their software (on the developer side) or performing a trial run of a new algorithm prior to use on their own data (on the user side). Recently, more open-source data sets have become available; for instance, Focant and co-workers have provided two data sets, consisting of either five fruit beers or nine flavored chocolate samples, that can be used to benchmark the performance of nontargeted methods. Using the chocolate data set, Weggler et al demonstrated that both the number of peaks aligned in the final peak tables and clustering on the PCA scores plot differed among eight commercial software packages for GC×GC data analysis .…”

“…Recently, more open-source data sets have become available; for instance, Focant and co-workers have provided two data sets, consisting of either five fruit beers or nine flavored chocolate samples, that can be used to benchmark the performance of nontargeted methods. Using the chocolate data set, Weggler et al demonstrated that both the number of peaks aligned in the final peak tables and clustering on the PCA scores plot differed among eight commercial software packages for GC×GC data analysis . Because it is difficult to ascertain the differences in proprietary vendor software, there has been increased interest in developing free and open-source software (FOSS). − With FOSS, the user can see exactly how the software treats their data and make changes to the software if necessary.…”