A novel approach for analyzing gas chromatography-mass spectrometry/olfactometry data

Abstract

GC-MS/O (gas chromatography-mass spectrometry/olfactometry) is an indispensable technique to associate individual volatile odorants to odors perceived by human assessors. Interpretation of GC-MS/O data is, however, hampered in practice by different factors related to the instrumental set-up and by heterogeneity among odor descriptions given by the assessors (olfactometer). In this paper, a novel automated approach is presented, which deals with these GC-MS/O challenges and enables visualization and interpretation of GC-MS/O data. It includes signal warping via COW (correlation optimized warping), synchronizing MS and O data via detection of odor areas and construction of a TOC (total odor count) to visualize odor heterogeneity, respectively. Our approach is implemented in practice, and we successfully associated odors to compounds in data sets of two alcoholic beverages with different flavor compositions. It leads to a faster and less biased association of odors to compounds compared to current practice, reducing the time and effort needed for interpreting GC-MS/O data.