Volatile organic compounds (VOCs) are important secondary metabolites in plants, with significant physiological, ecological, economic, and ornamental value. Gas chromatography-mass spectrometry (GC-MS) is a classical method for detecting and analyzing plant volatile aromas and is often regarded as the gold standard. However, its sample preparation is laborious and time-consuming, and real-time detection is difficult to achieve, which limits its application in large-scale sample analyses. Therefore, there is an urgent need to develop a method capable of accurate, real-time, and rapid detection of specific plant VOCs.

Proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) enables real-time quantitative analysis of complex VOC mixtures due to its high sensitivity, rapid response, and non-destructive soft ionization mechanism. Nevertheless, its inability to effectively distinguish between volatile isomers and the tendency for compound fragmentation prevent it from fully replacing the powerful qualitative capabilities of GC-MS. Integrating the strengths of both techniques could lead to an efficient and accurate VOC detection system. Using Hedychium plants as an example, this study systematically combines GC-MS and PTR-ToF-MS to establish a technical workflow aiming to identify marker ions that can rapidly predict the emission of specific VOCs, thereby improving the efficiency of aroma compound detection.

Using Hedychium plants as the study subject, this method details a technical framework that integrates GC-MS and PTR-ToF-MS to establish a rapid detection system for specific aromatic compounds. Key steps—including sample collection, parameter optimization, and data acquisition—are outlined. Additionally, a cross-platform data integration method based on partial least squares regression (PLSR) is introduced, along with detailed code execution steps. By constructing a robust and optimized prediction model, the PTR-ToF-MS spectral peak at m/z 155.144 was identified as a stable marker for predicting linalool emissions. This result was validated in two independent aroma datasets, demonstrating the successful application of PTR-ToF-MS for rapid quantification of target aromatic components.

This study addresses the limitations of GC-MS in large-scale sample applications by incorporating PTR- ToF-MS and integrating the advantages of both techniques. Using Hedychium as a model, a rapid detection system for specific aroma compounds was established, with detailed descriptions of the technical workflow and a cross-platform data integration method, including code implementation. A predictive model identified a robust marker for linalool emissions, validated across independent datasets, enabling rapid quantification of target aroma components via PTR-ToF-MS. These findings provide a new strategy for plant VOCs detection, with potential to advance detection technologies and improve efficiency and accuracy. The approach shows broad application prospects in plant physiology and ecology research, quality evaluation of economic crops, and aroma regulation in ornamental plants.