Chromatogram-level FID/MS data fusion in comprehensive two-dimensional gas chromatography enables robust quantitative...

Comprehensive two-dimensional gas chromatography (GC × GC) coupled with parallel flame ionization detection (FID) and mass spectrometry (MS) provides complementary quantitative and qualitative information, although the two detector signals are generally processed independently. Here, chromatogram-level FID/MS data fusion is evaluated as a preprocessing strategy for quantitative volatilomics using extra virgin olive oil (EVOO) as a benchmark matrix. FID and MS chromatograms were integrated into a single multidimensional chromatographic object and processed through a unified untargeted-targeted (UT) fingerprinting workflow. Preprocessing was systematically optimized by evaluating six signal-to-noise (S/N) thresholds. Compared with standalone detector processing, chromatogram-level fusion stabilized feature template generation, maintaining 30-35 reliable features across the investigated S/N range while reducing the dependence of fingerprinting performance on arbitrary preprocessing thresholds. The fused workflow combined the sensitivity of FID with the molecular specificity of MS, improving feature correspondence and enabling quantitative responses to be extracted directly from the matched chromatographic features. The optimized workflow enabled confident annotation of 121 volatile compounds and accurate quantification of 109 metabolites, including 38 historical quantitative markers consistently determined over six harvest seasons (2019-2025) across Italy, Spain and Brazil. The resulting harmonized database supported quantitative benchmarking and demonstrated that the chemically interpretable fraction of the volatilome preserves the dominant multivariate organization of the complete chromatographic fingerprint. Chromatogram-level FID/MS data fusion therefore provides a robust and scalable platform for GC × GC-based quantitative volatilomics, integrating chromatographic fingerprinting and accurate quantification within a single analytical workflow suitable for large-scale food quality assessment and authenticity studies.