Comparison of Traditional Chardonnay and Buttery Chardonnay with Data Processing for Sample Sets

Importance of the Topic

Gas chromatography coupled with time of flight mass spectrometry and advanced data processing enable detailed profiling of volatile compounds in complex matrices such as wine samples. Accurate characterization of aroma constituents supports quality control, product differentiation and sensory research in the beverage industry.

Objectives and Study Overview

This study compares two Chardonnay wines from the same vineyard and vintage—a traditional style and a buttery style—using headspace solid phase microextraction GC-TOFMS and ChromaTOF Sync data processing. The goal is to identify and quantify key volatile markers that correlate with sensory attributes and differentiate the two wine types.

Methodology and Instrumentation

• Sample preparation: Triplicate analysis of 2 mL aliquots using headspace SPME with a tri phase fiber at 40 °C for extraction.
• GC-TOFMS settings: Pegasus BT system, Stabilwax column, temperature program from 40 °C to 250 °C, mass range 35–300 m/z at 10 spectra/s acquisition.
• Data processing: ChromaTOF Sync for peak finding, deconvolution, retention index matching, and composite peak table generation across the six samples.

Main Results and Discussion

• Chromatographic profiles of both Chardonnay types show similar total ion currents, but multivariate analysis reveals clear separation on principal component 1.
• Key analytes identified in both samples include 3-methyl-1-pentanol, acetic acid and isopentyl hexanoate, with aroma notes of fermented, fruity and sour characteristics.
• Furfural is elevated in the buttery Chardonnay, reflecting bready and caramel notes from oak maturation and malolactic fermentation.
• Compounds enriched in the buttery style include acetoin and creosol, linked to creamy and spicy vanilla descriptors.
• The traditional style shows higher levels of 2-phenylethyl acetate, contributing floral and honeyed aromas typical of fresh fruit expression.

Benefits and Practical Applications

Advanced data processing with deconvolution enables detection of coeluting and low-level volatiles that may be missed by visual review. The generated compound trends support product authentication, quality monitoring and targeted flavor optimization for winemaking and beverage development.

Future Trends and Potential Applications

• Integration of larger spectral libraries and machine learning algorithms to improve compound identification accuracy.
• Real time monitoring of fermentation and aging processes using automated SPME sampling and rapid GC-TOFMS workflows.
• Coupling of chemical profiling with sensory and consumer data for predictive modeling of flavor perception.
• Extension of the workflow to other food and beverage matrices for comprehensive flavor mapping.

Conclusion

The combined use of HS-SPME GC-TOFMS and ChromaTOF Sync provides a robust approach for volatile profiling and comparison of wine samples. Specific markers reflecting buttery and fresh fruit characteristics were identified, demonstrating the value of full sample set processing and deconvolution for detailed aroma analysis.

Použité instrumentace

• GC-TOFMS system: Pegasus BT (LECO)
• Autosampler: LECO L-PAL 3 headspace SPME
• Column: Stabilwax 30 m × 0.25 mm × 0.25 µm

References

• Good Scents database, thegoodscentscompany.com