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

Area of Science:

• Analytical Chemistry
• Metabolomics
• Bioinformatics

Background:

• Gas chromatography-mass spectrometry (GC-MS) is crucial for metabolomics.
• Accurate peak resolution and retention time correction are vital for large-scale GC-MS batch analysis.
• Existing methods face challenges in resolving coeluting peaks and correcting retention time shifts.

Purpose of the Study:

• To introduce AntDAS-CPR, an integrated data analysis platform for untargeted GC-MS metabolomics.
• To enhance the resolution of coeluting peaks using a novel DEMCR-ALS algorithm.
• To provide a robust tool for accurate GC-MS data analysis.

Main Methods:

• Development of the AntDAS-CPR platform with modules for peak resolution, retention time correction, component registration, chemometrics, and identification.
• Optimization of the total ion chromatogram (TIC) peak resolution module using a dynamic elimination multivariate curve resolution-alternating least-squares (DEMCR-ALS) algorithm.
• Evaluation using standard mixtures and complex food matrix data.

Main Results:

• The DEMCR-ALS algorithm improved coeluting peak resolution and reduced reliance on initial estimates.
• AntDAS-CPR demonstrated superior performance compared to AMDIS, ADAP-GC, MS-DIAL, and eRah.
• The platform showed consistent outperformance in both targeted and untargeted GC-MS analyses.

Conclusions:

• AntDAS-CPR offers a significant advancement in GC-MS data analysis for metabolomics.
• The platform provides enhanced accuracy and reliability for complex biological and food samples.
• AntDAS-CPR is freely accessible, promoting wider adoption in scientific research.