Untargeted Chemical Profiling of Two-Dimensional Gas Chromatography Coupled with High-Resolution Mass Spectrometry Data for Botrytized Wines via Topological Data Analysis

Advanced chemical profiling of complex samples, such as botrytized wines, requires advanced analytical techniques capable of capturing subtle compositional variations. In this study, we introduce a statistically robust framework that leverages a topological data analysis (TDA) tool, Ball Mapper, in the context of comprehensive two-dimensional gas chromatography (GC × GC) with high-resolution time-of-flight mass spectrometry (HR-TOF-MS) to obtain untargeted identification of sample-specific chemical markers. A key design element of the proposed approach is its ability to numerically process the immense data volume generated per sample, whose statistical and chemical significance is often difficult to interpret using conventional methods. Each of the 34 wine samples yielded over 470,000 mass spectral functions, which were discretized, normalized, and clustered to obtain representative and relatively unique discrete mass spectral vectors in high-dimensional space. With only two interpretative parameters, the proposed framework uncovered 2,792 extracted mass spectral distributions, from which 1191 discriminative features were identified, including 334 compounds assigned to known volatile organic compound classes. The resulting chemical signatures reflected regional differences in fermentation style, grape variety, botrytization conditions, and microbial activity. Moreover, statistically robust framework of using Ball Mapper revealed consistent grouping patterns both within and between wines. These findings demonstrate that the proposed framework can support chemical characterization complex natural matrices and serve as a general strategy for analyzing any domains where GC × GC with HR-TOF-MS data are collected.