Use of automated sample preparation techniques for challenging sample by GC-MS

Importance of the topic

Automating sample preparation for challenging matrices in gas chromatography–mass spectrometry significantly enhances laboratory efficiency, safety and data quality. By replacing labor-intensive manual workflows, automated techniques address high throughput demands in flavor analysis, metabolomics and QA/QC while reducing operator exposure to solvents and minimizing variability.

Objectives and overview

This work reviews Anatune’s implementation of automated sample preparation solutions for GC-MS and related techniques. The goals are to demonstrate time and solvent savings, improved precision, and the capability to process diverse analyte classes—from very volatile compounds to semi-volatiles—using metabolomics derivatisation and a multivolatile extraction method (MVM).

Applied instrumentation

• Gerstel MultiPurpose Sampler (MPS2) with dual-head autosamplers
• Agilent GC–MS, GC–MS/MS, GC–QTOF, GC-QQQ platforms
• Thermal Desorption Unit (TDU) and Cooled Injection System (CIS)
• Dynamic Headspace (DHS) modules and multivolatile trapping assemblies

Methodology

The automated workflow integrates:

• Automated addition of internal standard and extraction solvent directly into vials
• 24/7 operation with prep-ahead scheduling to load worklists overnight
• Derivatisation for fatty acid methyl ester (FAME) analysis: automated methylation at 70 °C and liquid–liquid extraction into hexane
• Multivolatile Method (MVM): sequential trapping of very volatile, volatile/semi-volatile and fully evaporative fractions using tailored adsorbent traps under controlled nitrogen flow

Main results and discussion

• Time savings: For 60 extractions the analyst time dropped from 3.83 h manually to 1.18 h with automation (Δh = 2.65 h), enabling high throughput and reduced labor cost.
• Solvent reduction: Solvent consumption for 132 extracts decreased by approximately 2.5 L, translating into significant reagent cost savings.
• Reproducibility: FAME %CV ranged from 3.9 % to 6.9 %, reflecting tight precision in automated derivatisation and extraction.
• Trace sensitivity: DHS captured low-level analytes effectively, while the MVM successfully covered analytes from acetaldehyde to vanillin in aqueous matrices.
• Safety: Reduced manual handling of solvents minimized exposure and hazard risk.

Benefits and practical applications

• High-throughput flavor profiling and metabolomics studies with consistent sample processing
• QA/QC laboratories benefit from reliable calibration and reduced carryover
• 24/7 automated schedules increase data point collection for large studies
• Green chemistry advantages through solvent savings and waste reduction

Future trends and potential applications

• Integration of AI-driven decision tools to optimize sample prep parameters in real time
• Expansion to microfluidic and lab-on-chip platforms for ultra-low volume processing
• Coupling with novel detectors (e.g., ion mobility, orbitrap) for enhanced analyte coverage
• Further development of universal trapping materials for broader analyte classes

Conclusion

Automated sample preparation for GC-MS workflows delivers substantial improvements in throughput, reproducibility and safety while reducing solvent use and labor time. Techniques such as automated derivatisation and the multivolatile method provide versatile solutions for complex matrices, positioning laboratories to meet growing analytical demands.

Reference

No specific literature references were provided in the original text.