Characterization of automobile material emissions by TD-GC×GC-TOFMS (Romaine Klein, MDCW 2023)

• 🎤 Presenter: Romain Klein¹´²´³, José Dugay¹, Jérôme Vial¹, Didier Thiébaut¹, Donatien Barreteau², Guy Colombet², Arnaud Fournel³, Moustafa Bensafi³ (¹LSABM, UMR CBI 8231 CNRS, ESPCI Paris, PSL Université, Paris, France. ²Renault Group, Materials Engineering Department, Guyancourt, France. ³Lyon Neuroscience Research Center, University of Lyon, CNRS UMR5292, INSERM, Bron, France)

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15th Multidimensional Chromatography (MDC) Workshop 2024

Abstract

The study of pollutants and odorous molecules is of growing interest to automotive manufacturers within the frame of Vehicle Interior Air Quality (VIAQ) programs. The most common method to analyse material emissions is to heat them, collect their emissions on sorbent tubes, which are later thermally desorbed and analysed by gas chromatography coupled with mass spectrometry. This method is suitable for the target analysis of the dozen most intense molecules, but fails to uncover the true molecular complexity of the samples.

In the present work, we used a new instrumental configuration combining thermal desorption (TD), comprehensive bidimensional gas chromatography (GCxGC) and Time of Flight Mass Spectrometry (TOFMS) to elucidate the detailed composition of vehicle material emissions. This implied the testing of several column sets, including normal and reverse polarity configuration, and several second column dimensions, while managing constraints due to the TD coupling.

Over a hundred of potentially odorous polar compounds were observed for each sample, which would not have been possible on monodimensional GC due to low intensities and coelutions with apolar compounds present in large amount.

The material samples were also presented to an untrained panel of human noses while collecting their hedonic perception and semantic descriptions, in the aim of establishing links between the material emissions and perception. According to their subjective hedonistic rating, materials were classified into two groups, and this classification was compared to objective olfactomotor data.