Improving Quantitative Analysis of Red Wine Using the Xevo G2-XS QTof with SONAR Data Independent Acquisition (DIA)

Importance of the topic

In-depth characterization of phenolic compounds in red wine is essential for quality control, authenticity verification, and understanding health-related properties. Traditional targeted assays can quantify known analytes but lack the scope to detect unexpected components. Data independent acquisition (DIA) methods, especially SONAR, enable both targeted and non-targeted profiling in a single run, improving throughput and confidence in complex matrices such as wine.

Study objectives and overview

This study evaluates the performance of SONAR DIA on the Waters Xevo G2-XS QTof platform for simultaneous identification and quantitation of three key phenolics—trans-resveratrol, catechin, and p-coumaric acid—in red wine. The objectives include assessment of spectral clarity, sensitivity, linearity, and applicability across multiple wine varieties without prior method development.

Methodology

• Sample preparation: Eight commercial red wines from diverse grape varieties were diluted, centrifuged, and further diluted before spiking with 13C-labeled internal standards for matrix-matched calibration.
• Chromatography and MS: 10 μL injections; SONAR window of 30 Da scanning m/z 100–700; low-energy collision energy at 6 eV; high-energy ramp from 20 to 45 eV; time-of-flight detection m/z 50–1200 at 0.2 s acquisition rate.
• Calibration: Matrix-matched curves over 0.01–5 μg/mL or 0.1–5 μg/mL ranges using isotopically labeled analogs; LOD/LOQ defined by signal-to-noise ratios of 3 and 10 with ±5 ppm mass error.

Instrumentation

• Waters Xevo G2-XS QTof mass spectrometer
• SONAR data independent acquisition mode
• UNIFI Scientific Information System for data filtering and analysis

Main results and discussion

SONAR provided highly selective spectra comparable to targeted MS/MS without precursor selection. Spectral filtering in UNIFI enhanced clarity over conventional DIA, as illustrated for p-coumaric acid. Calibration curves showed excellent linearity (R2 > 0.999) and LODs as low as 0.01 μg/mL. Chromatographic peak widths of ~7 s with >12 points ensured robust quantitation. Concentrations of the three phenolics were successfully determined in all samples, demonstrating method applicability across eight wine varieties.

Benefits and practical applications

• Combined targeted quantitation and untargeted profiling in a single injection
• No method development or prior compound knowledge required for MS/MS-like spectra
• High sensitivity and linear dynamic range suitable for routine wine analysis
• Applicability to quality control, authenticity testing, and nutritional studies

Future trends and potential applications

The integration of SONAR with automated workflows and metabolomics databases will expand its application to broader food and beverage matrices. Future developments may include smaller SONAR windows for enhanced selectivity, real-time data processing for quality assurance, and coupling with ion mobility separation for deeper structural insights.

Conclusion

SONAR DIA on the Xevo G2-XS QTof enables accurate and sensitive quantification of key phenolic compounds in red wine while providing high-resolution spectra for unknown analytes. This flexible approach supports both targeted and non-targeted workflows, streamlining analysis without compromising data quality.

References

• Delgado de la Torre MP, Priego-Capote F, Luque de Castro MD. Characterization and Comparison of Wine Lees by Liquid Chromatography-Mass Spectrometry in High-Resolution Mode. Journal of Agricultural and Food Chemistry. 2017;63:1116–1125.
• Barnaba C, Dellacassa E, Nicolini G, et al. Targeted and untargeted high resolution mass approach for profiling glycosylated simple phenols in hybrid grapes. Food Research International. 2017;98:20–33.
• Ruocco S, Stefanini M, Stanstrup J, et al. The metabolomic profile of red non-Vitis vinifera genotypes. Food Research International. 2017;98:10–19.
• Heywood D, Craven K. SONAR: Delivering MS/MS Data from a DIA Experiment. Waters White Paper no. 720006033EN; 2017.
• Gethings L, Hughes C, Richardson K, et al. Metabolomic Workflow Utilizing Rapid Microbore Metabolic Processing (RAMMP) with SONAR. Waters Application Note no. 720006072EN; 2017.