Regression algorithm for calculating second-dimension retention indices in comprehensive two-dimensional gas chromatography

Gas chromatography-mass spectrometry (GC-MS) is one of the most accurate, well developed, and reliable analytical tools for the analysis of volatile and semivolatile compounds. The GC-MS data have been extensively improved by enhancing the separation capacity via comprehensive two-dimensional gas chromatography (GC x GC). The reliability of the identification of the analytes in GC x GC-MS can be notably improved by applying the second-dimension retention index (I-2) as additional analytical parameter along with the commonly used first dimension retention index (I-1) and mass spectrum. A novel approach for calculating second-dimension retention indices (I-2) for semivolatile organic compounds is proposed. It is noteworthy that the standards used in calculations are the same compounds recommended as internal standards by US EPA 8270 Method for analysis of semivolatile organic compounds. The new algorithm takes into account the analyte retention time and its retention temperature at the secondary column, ((2)t(R)) and (T-2(R)), respectively. The experimental data collected with different primary oven temperature ramp rates and carrier gas flow rates have shown that the calculated by the proposed approach I-2 values remain the same for each evaluated compound, drifting in a very narrow range. The proposed approach was tested using 100 organic compounds from various chemical classes including alkanes, phenols, nitrobenzenes, chlorinated hydrocarbons, anilines, polycyclic aromatic hydrocarbons (PAHs), phthalates, etc. The important advantage of the proposed I-2 values for compounds of the same chemical origin (reference standards and analytes) involves applicability of well-known Lee's indices for non-polar phases. Therefore, the proposed approach can be used in targeted and non-targeted analysis of a wide range of organic compounds. The reduced version of the second dimension retention indices provides a valuable mapping of the homologues series of organic compounds, making their detection and identification easy and reliable. (C) 2018 Published by Elsevier B.V.