Identification of coronavirus disease marker compounds in sweat with comprehensive two dimensional gas chromatography using multiloop splitter-based non-cryogenic artificial trapping modulation system

Abstract

This study applied comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GCMS–) for analysis of volatile compounds in headspaces of axillary swab samples of RT-PCR or antigen test kit proven COVID-19 positive and negative volunteers in Bangkok, Thailand. The separation was performed using a conventional Deans switch (DS) based heartcut system with an additional splitter system located between the first (1D) column (30 m) and the DS. The splitter consisted of deactivated fused silica (DFS) columns connected into three loops with the progressively doubled perimeters of the downstream loops. An artificial GC × GC result was obtained by applying a periodic heartcut (H/C) event within every artificial modulation period (PAM) of 1.14 min, selectively transferring the analytes onto the second (2D) column (30 m). Chemometric analysis including feature selection was used to identify significantly altered metabolites between the positive and negative groups. Fourteen significant metabolites were tentatively identified, including p-cymene, linalool, and 2,6,11-trimethyldodecane. The marker peak area thresholds were optimized by generating receiver operating characteristic (ROC) curves showing accuracy, sensitivity and selectivity within the ranges of 94–98%, 93–97% and 94–100%, respectively. Based on these results, we hypothesize that SARS-CoV-2 infection disrupts the metabolism of volatile metabolites in sweat, or impacts the microbiome, changing the volatile profile of sweat in infected patients.

Keywords

• Artificial modulation
• COVID-19 analysis
• Multidimensional GC
• Skin secretion
• Volatile markers