Volatilomics is an emerging discipline aimed at characterizing volatile metabolites in diverse matrices. Recently, comprehensive two-dimensional gas chromatography (GC × GC) coupled with parallel flame ionization detection (FID) and mass spectrometry (MS) has gained attention for combining accurate quantification with unambiguous compound identification. Traditionally, FID and MS data are processed independently. This study addresses their integration by merging FID and MS chromatograms into a single fused chromatogram, enhancing pattern recognition during template matching and enabling large-scale quantitative volatilomics. Feature matching is guided by MS spectral similarity, minimizing mismatches and extracting FID responses for robust quantification. The contribution discusses the workflow designed to obtain combined detector signals and the challenges posed by dual parallel detection operated under both thermal and differential-flow modulation, dual-parallel second-dimension column configurations, and differences in acquisition frequency between detectors. From an application standpoint, chromatogram fusion directly responds to emerging analytical needs in volatilomics—supporting quantitative, high-throughput analysis across extended time frames through the FID channel, while ensuring the mandatory MS confirmation required for the reliable identification of fragrance allergens and regulated compounds. The resulting fused chromatogram consolidates complementary detector information within a single multidimensional chromatogram, improving data consistency, interpretability, and throughput. Overall, chromatogram-level fusion represents a key step toward integrated, multimodal analytical platforms for robust and scalable volatilomics workflows.

Chromatogram-level fusion of FID and MS signals in GC × GC for quantitative volatilomics: workflow design and impact on pattern recognition

Caratti, Andrea;Fina, Angelica;Trapani, Fulvia;Squara, Simone;Liberto, Erica;Bicchi, Carlo;Cordero, Chiara
2026-01-01

Abstract

Volatilomics is an emerging discipline aimed at characterizing volatile metabolites in diverse matrices. Recently, comprehensive two-dimensional gas chromatography (GC × GC) coupled with parallel flame ionization detection (FID) and mass spectrometry (MS) has gained attention for combining accurate quantification with unambiguous compound identification. Traditionally, FID and MS data are processed independently. This study addresses their integration by merging FID and MS chromatograms into a single fused chromatogram, enhancing pattern recognition during template matching and enabling large-scale quantitative volatilomics. Feature matching is guided by MS spectral similarity, minimizing mismatches and extracting FID responses for robust quantification. The contribution discusses the workflow designed to obtain combined detector signals and the challenges posed by dual parallel detection operated under both thermal and differential-flow modulation, dual-parallel second-dimension column configurations, and differences in acquisition frequency between detectors. From an application standpoint, chromatogram fusion directly responds to emerging analytical needs in volatilomics—supporting quantitative, high-throughput analysis across extended time frames through the FID channel, while ensuring the mandatory MS confirmation required for the reliable identification of fragrance allergens and regulated compounds. The resulting fused chromatogram consolidates complementary detector information within a single multidimensional chromatogram, improving data consistency, interpretability, and throughput. Overall, chromatogram-level fusion represents a key step toward integrated, multimodal analytical platforms for robust and scalable volatilomics workflows.
2026
418
7
2027
2044
Comprehensive two-dimensional gas chromatography; Data fusion; Image pattern recognition; Parallel detection; Quantitative volatilomics; UT fingerprinting
Caratti, Andrea; Fina, Angelica; Trapani, Fulvia; Squara, Simone; Liberto, Erica; Tao, Qingping; Geschwender, Daniel; Heble, Chase; Reichenbach, Steph...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2132331
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