AromaOffice: Application of a Novel Linear Retention Indices Database to a Complex Hop Essential Oil

Importance of the Topic

Gas chromatographic retention indices (RIs) combined with mass spectral (MS) data form a powerful two-dimensional framework for confident compound identification in flavor and fragrance analysis. In complex matrices such as hop essential oil, reliable discrimination among closely related terpenoids and trace sulfur or nitrogen compounds is critical for quality control, sensory evaluation and product development.

Objectives and Overview of the Study

This application note describes the use of Aroma Office 2D software for integrated processing of both RI and MS data. The main goals were:

• To demonstrate automated RI calculation and MS library searching on single-dimensional GC columns (DB-Wax and DB-5).
• To implement orthogonal cross-searching of RI values from two different columns for secure compound identification.
• To illustrate the role of detector-specific signals (sulfur, nitrogen) and GC-O in resolving weak MS responses.

Methodology and Instrumentation

Hop essential oil samples were analyzed by GC-MS and GC-O using the following configuration:

• Gas chromatograph: Agilent 7890 with S/SL injector (split 1:100 at 250 °C)
• Columns: 30 m × 0.25 mm ID, 0.25 µm DB-Wax and 30 m × 0.25 mm ID, 0.25 µm DB-5
• Pneumatic conditions: Helium at constant flow of 1.0 mL/min
• Oven program: 40 °C (2 min), ramp 5 °C/min to 240 °C (hold 18 min)
• MS detector: 5977 MSD in full scan (29–300 amu, 2.68 scans/sec)
• Element-selective detectors: Nitrogen-phosphorus detector (NPD) and Flame photometric detector (FPD) for sulfur
• Software: Aroma Office 2D integrated into Agilent ChemStation
• Olfactory port: ODP 3 coupled to GC-O

Main Results and Discussion

The workflow and key findings include:

• Automated calculation of RI values on each column for all TIC peaks and simultaneous PBM MS library searching.
• Single-column searches on DB-Wax produced 533 candidates; DB-5 searching yielded 392 candidates for a target peak with strong onion-like odor.
• Orthogonal RI cross-searching between DB-Wax (RI 1392) and DB-5 (RI 972) narrowed candidates to seven structures.
• Elemental detection (FPD positive, NPD negative) indicated a sulfur-containing compound.
• Database cross-search with sulfur constraint identified dimethyl trisulfide as the only matching candidate; confirmation was obtained by standard injections on both columns.
• Additional examples (α-Gurjunene vs. Valencene) illustrated rejection of false PBM hits when RI values did not match database entries.

Benefits and Practical Applications of the Method

This integrated approach offers:

• Streamlined identification combining RI and MS in a single software environment.
• Reduced false positives through orthogonal confirmation across two columns and elemental detectors.
• Faster throughput in flavor and fragrance QC, R&D and product authentication.
• Enhanced sensitivity to low-abundance odorants via GC-O and targeted cross-searching.

Future Trends and Potential Applications

Advances likely to expand these capabilities include:

• Multidimensional GC-O/MS with heart-cutting or comprehensive 2D separations for ultra-complex mixtures.
• Predictive retention index modeling and machine-learning–assisted library matching.
• Online enrichment techniques (PFC) to isolate trace odorants before MS detection.
• Expansion of RI databases to include emerging natural and synthetic flavor compounds.

Conclusion

Aroma Office 2D centralizes RI and MS data processing, automates cross-searching across orthogonal columns and supports elemental and olfactory detection. This integrated workflow significantly enhances confidence in compound identification in complex flavor matrices such as hop essential oil, streamlines laboratory operations and reduces reliance on manual data handling.

References

1. Shellie R., Mondello L., Marriott P., Dugo G. Characterisation of lavender essential oil by using GC-MS with correlation of linear retention indices and comparison with comprehensive 2D GC. J. Chromatogr. A.
2. d’Acampora Zellner B., Bicchi C., Dugo P., Rubiolo P., Dugo G., Mondello L. Linear retention indices in gas chromatographic analysis: a review. Flavour Fragr. J.
3. Marriott P.J., Shellie R., Cornwell C. Gas chromatographic technologies for the analysis of essential oils. Anal. Chim. Acta.