A GC–MS Data Analysis Platform for Untargeted Metabolomics with Enhanced Coeluting Peak Resolution

Gas chromatography–mass spectrometry (GC–MS) remains challenged by the accurate resolution of coeluting peaks and the correction of retention time shift in large-scale batch analysis. Here, we introduce AntDAS-CPR, an integrated data analysis platform tailored for untargeted GC-MS-based metabolomics. The platform incorporates modules for total ion chromatogram (TIC) peak resolution, retention time shift correction, component registration, chemometric analysis, and compound identification. In this work, the TIC peak resolution module was specifically optimized through the development of a dynamic elimination multivariate curve resolution–alternating least-squares (DEMCR-ALS) algorithm, which employs multiple initialization strategies to enhance the resolution of coeluting peaks and reduce dependence on initial estimates inherent in conventional methods. The performance of AntDAS-CPR was comprehensively evaluated using standard mixtures and complex food matrix data sets. It was compared with state-of-the-art GC–MS data analysis tools, including AMDIS, ADAP-GC, MS-DIAL, and eRah. Comparative results demonstrated that AntDAS-CPR consistently outperforms existing methods in both targeted and untargeted analysis. The platform is freely accessible at http://www.pmdb.org.cn/antdascpr.