A Non-Targeted Metabolomic Study of Pomegranate Juice to Investigate the Nutritional and Quality Characteristics Using Novel SONAR DIA Acquisition and Vion Ion Mobility QTof MS

Importance of the topic

Profiling the full range of phytochemicals in fruit juices is essential for quality control, authenticity assessment and understanding health-related properties. Pomegranate juice contains a diverse array of phenolic compounds whose profiles vary with processing method. Reliable, non-targeted metabolomic approaches help the food industry verify product integrity, detect adulteration and support nutritional claims.

Study objectives and overview

This study evaluates a novel Data Independent Acquisition (SONAR DIA) strategy combined with ion mobility-enabled QTof mass spectrometry (IM-QTof-MS) for non-targeted metabolomic analysis of commercially available pomegranate juices. The goals were to:

• Differentiate juice types (not-from-concentrate, from-concentrate, fresh arils).
• Identify characteristic phytoactive markers.
• Demonstrate improved spectral clarity and confidence in compound annotation.

Methodology

Juice samples (n=12) were diluted, sonicated and centrifuged prior to filtration. UPLC separation was performed on an ACQUITY UPLC HSS T3 column (2.1×100 mm, 1.8 µm) with a water/ammonium acetate–acetonitrile gradient at 0.4 mL/min. Negative-mode electrospray ionization data were acquired in alternating low- and high-energy channels over 50–1 200 m/z, using a scanning quadrupole window (100–500 Da) for SONAR DIA.

Used instrumentation

• Waters ACQUITY UPLC I-Class system
• Xevo G2-XS QTof with SONAR DIA capability
• Vion IMS QTof mass spectrometer
• Progenesis QI and EZinfo software for multivariate and database searches

Main results and discussion

Principal component analysis (PCA) of Progenesis QI data revealed distinct clusters for each juice category, with a pooled QC in the center. Fresh arils and not-from-concentrate juices were enriched in flavonoid O-glycosides (e.g. myricetin-3-galactoside), whereas from-concentrate samples contained synthetic dyes and preservatives (e.g. Pigment Red 149). Orthogonal PLS-DA and S-plots pinpointed marker ions. SONAR DIA provided cleaner spectra by isolating co-eluting precursors, improving fragmentation scores and mass accuracy compared to conventional DIA. Ion mobility separation further resolved isobaric acids (citric/isocitric), a key authenticity metric.

Benefits and practical applications

• Enhanced spectral selectivity and reduced complexity in non-targeted workflows.
• High scan rates (>2 000 spectra/s) compatible with high-resolution MS.
• Improved confidence in compound identification through better precursor–fragment association.
• Single-run profiling of complex food matrices for QC and authenticity.

Future trends and potential applications

Combining SONAR DIA with ion mobility is poised to become a standard in food metabolomics, enabling:

• Broad screening of botanical products and beverages.
• Rapid detection of adulterants and process-related additives.
• Integration with machine learning for automated marker discovery.
• Extension to clinical metabolomics and environmental analysis.

Conclusion

This work demonstrates that SONAR DIA on a scanning quadrupole QTof, especially when paired with ion mobility, delivers robust non-targeted metabolomic profiling of pomegranate juice. The approach yields high-quality spectra, clear differentiation of juice types and reliable marker identification, supporting its adoption in food quality and authenticity testing.

References

• Mena P., et al. Rapid and Comprehensive Evaluation of (Poly)phenolic Compounds in Pomegranate (Punica granatum L.) Juice by UHPLC-MSn. Molecules. 2012;17:14821–14840.
• Gethings L.A., et al. Lipid Profiling of Complex Biological Mixtures by LC/MS Using a Novel Scanning Quadrupole DIA Strategy. Rapid Commun Mass Spectrom. 2017;31(19):1599–1606.
• Moseley H.N.B., et al. Scanning Quadrupole Data-Independent Acquisition. Part A: Qualitative and Quantitative Characterization. J Proteome Res. 2018;17(2):770–779.