Comprehensive two-dimensional gas chromatography with flow modulator coupled via tube plasma ionization to an atmospheric pressure high-resolution mass spectrometer for the analysis of vermouth volatile profile

The analysis of complex samples is a big analytical challenge due to the vast number of compounds present in these samples as well as the influence matrix components could cause in the methodology. In this way, comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC x GC-MS) is a very powerful tool to achieve the characterization of complex samples. Nevertheless, due to possible coelutions occurring in these matrices, mixed spectra are generally obtained with electron ionization (EI) which could extremely complicate the identification of the analytes. Thereby, new methodology setups are required to improve the confidence on the identification in non-targeted determinations. Here, we present a high-throughput methodology consisting of GC x GC with flow modulation coupled to high-resolution atmospheric pressure mass spectrometry (HRMS) via a novel tube plasma ion source (TPI). The flow modulator allows to easily automate the GC x GC method compared to traditional cryo-modulators, while the soft ionization provided by TPI helps to preserve the [M](+center dot) or [M+H](+) ions, thus increasing the confidence in the identification. Additionally, the combination of a flow modulation with an atmospheric pressure mass spectrometer significantly improves the sensitivity over flow modulated GC x GC-EI-MS methods because no split is required. This methodology was applied to the analysis of a complex sample such as vermouth where the volatile profile is usually considered by consumers as a product quality indicator since it raises the first sensations produced during its consumption. Using this approach, different classes of compounds were tentatively identified in the sample, including monoterpenes, terpenoids, sesquiterpenoids and carboxylic acid, and carboxylate esters among others, showing the great potential of a GC x GC-TPI-qTOF-MS platform for improving the confidence of the identifications in non-targeted applications.