separations Article Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data Seo Lin Nam , A. Paulina de la Mata and James J. Harynuk *   Citation: Nam, S.L.; de la Mata, A.P.; Harynuk, J.J. Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data. Separations 2021, 8, 84. https:// doi.org/10.3390/separations8060084 Academic Editor: Alena Kubatova Received: 18 May 2021 Accepted: 10 June 2021 Published: 15 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Department of Chemistry, University of Alberta, 11227 Saskatchewan Drive, Edmonton, AB T6G 2G2, Canada; seolin@ualberta.ca (S.L.N.); delamata@ualberta.ca (A.P.d.l.M.) * Correspondence: james.harynuk@ualberta.ca; Tel.: +1-780-492-8303; Fax: +1-780-492-8231 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. Keywords: comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS); scripting; metabolomics; data analysis; data visualization 1. Introduction Most of the GC-based metabolomics applications combine GC with MS detection to help with the identification of unknown analytes. Metabolomics samples typically exhibit high complexity due to their diverse chemical content that is present at wide concentration ranges. In non-target studies, accurate identification of metabolites at low concentrations can be complicated by coelutions and/or peak distortion due to closely/coeluting highly abundant metabolites [1,2]. Low-concentration analytes can also be easily obscured due to noise in the spectrum that can hinder the qualitative identification of peaks based on mass spectral library matching. Meanwhile, overloaded peaks from high-concentration species may lead to inaccurate identification arising from detector saturation and distortion of mass spectra [3]. As a platform, comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) is an excellent tool for non-target metabolomics. The higher and more effective use of peak capacity when compared to one-dimensional GC methods, results in improved signal-to-noise ratios due to increased signal (focus- ing/band compression at modulator) and decreased noise (separation of analytes from primary column bleed and coeluting analytes). Consequently, spectra are cleaner, allowing improved compound identification. When compared to LC-MS methods, matrix effects are less in GC-MS, and the technique offers a broad dynamic range [4]. Additionally, GC×GC techniques provide chromatograms with an inherently ordered structure, which is useful for the identification of unknown compounds. Moreover, this technique is advantageous Separations 2021, 8, 84. https://doi.org/10.3390/separations8060084 https://www.mdpi.com/journal/separations