New strategies for non-targeted quantification in comprehensive two-dimensional gas chromatography: The potential of reconstructed TIC response factor surfaces

Abstract

Comprehensive two-dimensional gas chromatography (GC × GC) offers unrivaled peak capacity and the ability to resolve and characterize hundreds to thousands of compounds within complex environmental samples. Knowledge of their concentrations is essential in assessing their toxicological and regulatory relevance; however, reliable quantification typically requires concurrent analysis of an authentic standard for each compound. Response factors (RF) may be used to estimate the concentration of a compound without the need for its respective standard, but this is not routinely employed in gas chromatography-mass spectrometry (GC–MS). This study demonstrates that GC × GC offers a new opportunity for the calculation of RFs and estimating a compounds concentration via the creation of 2D total ion current (TIC) response factor surfaces (RFS). A new method, linking a compounds retention time coordinate to its RF using a reconstructed TIC approach, was used to calculate the concentration of 27 compounds in both solvent and effluent wastewater matrix. Using both suspect screening and non-targeted methodologies, average fold change errors of 1.58 and 1.83 respectively were determined for the novel RFS approach. The approach was compared to an RF prediction model utilizing molecular descriptors, using a genetic algorithm, partial least squares regression (GA-PLS) model. The accuracy of the RFS method demonstrates its potential in the fields of suspect screening and non-targeted analysis, where realistic concentration estimates are required. The RFS approach delivers comparable accuracy in concentration prediction compared to computational approaches for determining RFs, but crucially, without the need for generating molecular descriptors or acquiring a reference standard. Additionally, a purely non-targeted approach outlined can allow for the calculation of RF for features without the need for identification or a reference spectrum.