Journal of Chromatography A 1627 (2020) 461401 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma Statistical inference of mass channel purity from Fisher ratio analysis using comprehensive two-dimensional gas chromatography with time of flight mass spectrometry data Grant S. Ochoa a,1 , Sarah E. Prebihalo a,1 , Brooke C. Reaser a,2 , Luke C. Marney b , Robert E. Synovec a,∗ a Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98195, USA b Department of Chemistry, Seattle University, 901 12th Avenue, Seattle, WA 98122, USA a r t i c l e i n f o Article history: Received 23 May 2020 Revised 8 July 2020 Accepted 9 July 2020 Available online 10 July 2020 Keywords: Gas chromatography Mass spectrometry Fisher ratio Quantification a b s t r a c t Tile-based Fisher ratio (F-ratio) analysis has recently been developed and validated for discovery-based studies of highly complex data collected using comprehensive two-dimensional gas chromatography cou- pled with time-of-flight mass spectrometry (GC×GC-TOFMS). In previous studies, interpretation and uti- lization of F-ratio hit lists has relied upon manual decomposition and quantification performed by chemo- metric methods such as parallel factor analysis (PARAFAC), or via manual translation of the F-ratio hit list information to peak table quantitative information provided by the instrument software (ChromaTOF). Both of these quantification approaches are bottlenecks in the overall workflow. In order to address this issue, a more automatable approach to provide accurate relative quantification for F-ratio analyses was investigated, based upon the mass spectral selectivity provided via the F-ratio spectral output. Diesel fuel spiked with 15 analytes at four concentration levels (80, 40, 20, and 10 ppm) produced three sets of two class comparisons that were submitted to tile-based F-ratio analysis to obtain three hit lists, with an F-ratio spectrum for each hit. A novel algorithm which calculates the signal ratio (S-ratio) between two classes (eg., 80 ppm versus 40 ppm) was applied to all mass channels (m/z) in the F-ratio spectrum for each hit. A lack of fit (LOF) metric was utilized as a measure of peak purity and combined with F- ratio and p-values to study the relationship of each of these metrics with m/z purity. Application of a LOF threshold coupled with a p-value threshold yielded a subset of the most pure m/z for each of the 15 spiked analytes, evident by the low deviations (< 5%) in S-ratio relative to the true concentration ratio. A key outcome of this study was to demonstrate the isolation of pure m/z without the need for higher level signal decomposition algorithms. © 2020 Elsevier B.V. All rights reserved. 1. Introduction Comprehensive two-dimensional (2D) gas chromatography (GC×GC), pioneered in 1991 by Liu and Phillips [1], has been ex- tensively developed and is a powerful tool for the analysis of com- plex mixtures [2–14]. While traditional one-dimensional gas chro- matography (1D-GC) can efficiently separate, identify and quan- tify volatile and semi-volatile analytes, the separation power may be inadequate for many complex samples [15]. Indeed, addition ∗ Corresponding author. E-mail address: synovec@chem.washington.edu (R.E. Synovec). 1 These authors contributed equally to this work. 2 Current address: Analytical Chemistry, L’Oréal USA Research & Innovation, 30 Terminal Ave | Clark, NJ 07083, USA. brooke.reaser@rd.loreal.com of the second chromatographic dimension with a complementary stationary phase can increase the separation and resolving power of GC×GC over 1D-GC about 10-fold [16–19]. When coupled with time-of-flight mass spectrometry (TOFMS), GC×GC-TOFMS facili- tates improved identification and quantification of trace level an- alytes [3,5,6,14,20]. The inherent complexity of GC×GC-TOFMS data, with its third order data structure [20,21] presents unique data analysis bene- fits and challenges, which are further impacted by the addition of a fourth dimension due to consideration of multiple sample classes as defined by the experimental design [20,21]. Fortunately, significant effort directed at developing chemometric tools to elu- cidate chemical information from GC×GC-TOFMS data have im- proved the quality of results obtained [18]. Broadly, chemometric methods aim to analyze analytical data qualitatively (signal de- https://doi.org/10.1016/j.chroma.2020.461401 0021-9673/© 2020 Elsevier B.V. All rights reserved.