Analysis of Pinot Noir Wines by HS-SPME GC/Q-TOF: Correlating Geographical Origin with Volatile Aroma Profiles

Importance of Topic

Pinot noir wines display complex volatile aroma profiles that reflect vineyard origin. Understanding these aroma markers supports quality control, authenticity verification and guides viticulture practices across diverse terroirs.

Study Objectives and Overview

This study aimed to analyze 45 Pinot noir wine samples from 15 vineyards in California and Oregon using headspace solid phase microextraction coupled with accurate-mass gas chromatography quadrupole time-of-flight mass spectrometry. Triplicate fermentations and replicate analyses ensured statistical robustness. Multivariate analysis was applied to correlate volatile signatures with geographical origin.

Methodology

• Sample preparation: 10 mL wine aliquots with sodium chloride in sealed headspace vials
• HS-SPME conditions: 100 µm fiber, equilibration at 30 °C for 5 min, extraction for 45 min, desorption at 240 °C
• GC/Q-TOF parameters: DB-WAXetr column, temperature program from 40 °C to 240 °C, acquisition 33–300 m/z at 4 GHz
• Data processing: relative peak areas normalized to internal standard, ANOVA and PCA using statistical software

Instrumentation

• Agilent PAL autosampler configured for HS-SPME
• Agilent 7200B GC/Q-TOF for high-resolution accurate-mass detection
• DB-WAXetr capillary column

Main Results and Discussion

Over 250 peaks per sample were detected, with 66 key compounds identified or tentatively assigned, including alcohols, esters, terpenes and sesquiterpenes. Trace levels of 2,4,6-trichloroanisole were quantified at parts-per-quadrillion sensitivity. PCA separated samples by region, distinguishing Oregon from California north and central coast AVAs. Regional clusters were driven by specific aroma markers revealed in loadings analysis.

Benefits and Practical Applications

This approach provides a sensitive and reproducible workflow for fingerprinting wine aroma profiles. It can be employed for provenance authentication, quality assurance, and to inform vineyard management practices by relating microclimate and soil factors to sensory attributes.

Future Trends and Applications

• Integrate vineyard weather stations and microclimate data for multiyear studies
• Expand elemental and polyphenolic profiling complementing volatile analysis
• Apply sensory-chemometric models and machine learning for predictive viticulture

Conclusion

HS-SPME GC/Q-TOF proved highly sensitive and accurate for analyzing Pinot noir volatile profiles across diverse AVAs. Multivariate models successfully correlated aroma signatures with vineyard origin, highlighting the method’s utility for enological research and quality control.