Using Headspace SPME-GC×GC–quadMS for the Characterization of Marsala Wine

Importance of the Topic

Marsala wine is a distinguished Sicilian dessert wine valued for its complex aroma profile and global economic significance. Characterizing the volatile fraction of such wines is crucial for quality control, authentication and understanding sensory attributes.

Objectives and Study Overview

This work aimed to map the headspace composition of four Marsala wine types (“fine”, “superiore secco”, “superiore riserva dolce” and “vergine”) using a headspace solid-phase microextraction (HS-SPME) method combined with comprehensive two-dimensional gas chromatography and quadrupole mass spectrometry (GC×GC–quadMS) with flame ionization detection (FID). Researchers sought both qualitative identification and semi-quantitative analysis of volatile constituents.

Methodology and Instrumentation

Samples were prepared by exposing a DVB/CAR/PDMS fibre to 4 mL of wine in a 20 mL vial at ambient temperature for 30 minutes with agitation at 500 rpm. After extraction, the fibre was thermally desorbed in splitless mode at 270 °C. A two-dimensional GC configuration used a nonpolar SLB-5ms primary column and a polar Supelcowax-10 secondary column. The effluent was split equally between FID for relative quantification and a fast-scanning quadrupole MS for compound identification.

Used Instrumentation

• Shimadzu GC-2010 two-dimensional gas chromatographs
• GCMS-QP2010 Ultra quadrupole mass spectrometer
• Shimadzu AOC-5000 headspace autosampler
• Loop-type cryogenic modulator
• Primary column: SLB-5ms (30 m × 0.25 mm ID, 0.25 µm df); Secondary column: Supelcowax-10 (1 m × 0.1 mm ID, 0.1 µm df)

Main Results and Discussion

Across all samples, 128 volatile compounds were tentatively identified, with individual wine types showing 87 to 91 compounds each. The volatile profile included esters, alcohols, acids, aldehydes, ketones, hydrocarbons and ethers. Key findings:

• Esters accounted for 19 % of the headspace, with ethyl octanoate as the dominant ester, decreasing from 40.9 % in the youngest wine to 19.4 % in the oldest.
• Acids represented 15 % of the composition; diethyl succinate increased with ageing from 2.7 % to 9.2 %.
• Alcohols were the largest class (48 %), with isopentyl alcohol comprising 18–21 % across samples.
• Aldehydes and ketones together made up 7 %, with furfural particularly abundant in older reserves.

The enhanced peak capacity of GC×GC effectively resolved co-eluting compounds that would overlap in one-dimensional GC, revealing over 500 detectable features.

Benefits and Practical Applications

This method delivers an in-depth volatile fingerprint useful for wine quality control, authentication and process optimization. The combination of HS-SPME and GC×GC–quadMS/FID offers:

• High sensitivity for trace volatiles
• Comprehensive coverage of diverse chemical classes
• Semi-quantitative data via FID and definitive identification by MS

Future Trends and Opportunities

Advancements may include integration with time-of-flight MS for deconvolution of co-elutions and the development of targeted workflows for marker compounds linked to sensory attributes. Miniaturized or online automation could further streamline headspace sampling in routine wine analysis.

Conclusion

The HS-SPME GC×GC–quadMS/FID workflow reveals the intricate volatile makeup of Marsala wines, demonstrating superior separation power and reliable semi-quantitative profiling. This approach establishes a foundation for future studies in wine authenticity, ageing effects and aroma chemistry.

References

Dugo F A, Tranchida P Q, Dugo P, Mondello L. HS-SPME GC×GC–quadMS method for the analysis of Marsala wine. Food Chemistry. 142 (2014) 262–268.