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. 2020 Oct 20;92(20):13953-13960.
doi: 10.1021/acs.analchem.0c02844. Epub 2020 Oct 9.

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

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Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

Michael J Wilde et al. Anal Chem. .

Abstract

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high-complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1tR and 2tR was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Flow diagram depicting the complexity and layers of GC×GC data processing within a wider data workflow (preprocessing includes steps such as baseline correction and signal smoothing. Postprocessing includes steps such as feature extraction/peak detection. Data reduction can include application of multivariate analysis and machine learning techniques).
Figure 2
Figure 2
Flow diagram showing steps for using the command-line interface, extending beyond a commercial software GUI with the creation of a command and batch file where open-source code can be integrated, increasing the interconnectivity of chemometric tools.

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