GC/MS Metabolomics with Automated Sample Preparation and Measurement followed by AI- assisted Data Analysis and Discussion

Importance of the Topic

The automated integration of sample preparation, gas chromatography–mass spectrometry, and AI-assisted data processing addresses key challenges in high-throughput metabolomic profiling of complex herbal matrices. Rapid, reproducible extraction and derivatization, combined with precise peak integration and statistical analysis, enable timely insights into the composition and bioactive components of herbal medicines.

Objectives and Study Overview

This study demonstrates an end-to-end platform for the analysis of three commercially available powdered Chinese herbal medicines (five replicates each). Objectives include:

• Automating sample preparation to reduce manual workload and variability.
• Performing GC-MS measurement of over 400 targeted metabolites in 23 minutes using MRM mode.
• Applying AI for accurate peak integration and generative interpretation of results.
• Conducting multivariate statistical analysis to identify characteristic metabolites.

Methodology and Instrumentation

Sample preparation was carried out on an SPL-M100 online SPE-GC system, performing solid-phase derivatization and headspace analysis with minimal manual intervention. GC-MS measurements used a GCMS-TQ8040 NX with helium carrier gas and a temperature gradient (100°C to 330°C) over 23 minutes. Data processing involved:

• Peakintelligence for AI-driven peak integration, improving accuracy over traditional algorithms.
• Multi-Omics Analysis Package (Garuda) for statistical workflows including PCA, hierarchical clustering, and volcano plots.
• Chatcata generative AI for secure, context-aware interpretation and discussion planning.

Key Results and Discussion

The combined platform detected approximately 250 metabolites across the three herbal medicines. Automation reduced sample preparation time from 1.5 days to 15 minutes per sample, achieving a 144-fold improvement in throughput. For a batch of 30 samples, total analysis time decreased from 55 hours to 18 hours, saving 37 labor hours. AI-assisted peak integration increased precision and reduced data processing time from 2.5 to 1.75 hours.
Multivariate analysis revealed distinct metabolite profiles characteristic of each herbal matrix. PCA and clustering separated samples by compositional signatures, while volcano plot analysis highlighted differentially abundant compounds, such as purine derivatives including hypoxanthine. Generative AI facilitated rapid interpretation and guided planning of follow-up experiments on energy metabolism pathways.

Benefits and Practical Application

The integrated workflow offers:

• Consistent derivatization-to-injection timing to enhance reproducibility.
• Reduced contamination risk from high-sugar samples via automated online cleanup.
• Space and equipment savings by consolidating multiple devices.
• Accelerated decision-making supported by AI-driven data interpretation.

Future Trends and Potential Applications

Emerging directions include the integration of cloud-based analytics, expanded AI-driven methods for untargeted metabolomics, real-time adaptive control of sample workflows, and cross-platform data fusion with proteomics and lipidomics. Secure generative AI agents may further streamline method development and report generation in regulated environments.

Conclusion

The combination of SPL-M100 automated sample preparation, GCMS-TQ8040 NX analysis, and AI-assisted data processing delivers a high-throughput, reproducible platform for herbal medicine metabolomics. The approach significantly reduces manual labor and accelerates statistical interpretation, enabling faster insight into complex natural product matrices.

Instrumentations Used

• GCMS-TQ8040 NX gas chromatograph–triple quadrupole mass spectrometer
• SPL-M100 online SPE-GC system
• Peakintelligence AI signal processing
• Multi-Omics Analysis Package (Garuda)
• Chatcata generative AI

References

• Online SPE-GC System for Metabolome Analysis SPL-M100. AiSTI SCIENCE Co. Ltd, accessed December 5, 2023.
• Time-saving data processing for pesticide residues with Peakintelligence for GCMS, Application News No. 01-00585-EN.