AUTOMATED SOLVENT EXTRACTION (LLME AND DLLME) OF FLAVOURS FROM A SELECTION OF FRUIT BEVERAGES

Significance of the topic

Flavor profiling of fruit beverages is critical for maintaining product quality, ensuring consumer satisfaction and complying with regulatory standards. Automated microextraction techniques such as LLME and DLLME offer rapid, solvent-efficient alternatives to traditional methods, supporting high-throughput screening and detailed aroma characterization.

Study objectives and overview

This study evaluated automated dispersive liquid-liquid microextraction (DLLME) and liquid-liquid microextraction (LLME) using a GERSTEL MPS platform coupled to Agilent GC-MS, and compared results with headspace solid-phase microextraction (HS-SPME). A selection of fruit-flavored waters, juice drinks and 100% orange juice were profiled to assess extraction efficiency and compound coverage.

Methodology

The workflow comprised:

• Sample set: bottled water blank, flavored water (orange/pineapple), apple/blackcurrant juice drink, orange juice drink and freshly squeezed orange juice.
• DLLME for flavored drinks: 6 mL sample mixed with 600 µL isopropanol and 500 µL DCM/pentane (80/20), vortexed, centrifuged; bottom layer injected (1 µL splitless or 10 µL solvent vent).
• LLME for orange juice: 6 mL sample with 2000 µL hexane, vortexed, centrifuged; top hexane layer injected.
• HS-SPME: 6 mL in 20 mL vial incubated at 40 °C for 20 min; DVB/CAR/PDMS fiber exposed 10 min, desorbed at 250 °C.
• GC-MS conditions: Agilent 7890 B/5977 MS (HES source), DB-FFAP column (30 m×0.25 mm×0.25 µm), He 1.2 mL/min, oven 40 °C→200 °C @5 °C/min→250 °C @10 °C/min.

Main results and discussion

DLLME extracts of flavored drinks revealed distinct chromatographic profiles rich in medium-to-low volatility compounds, whereas HS-SPME favored early eluting, highly volatile analytes. The IPA+DCM/pentane mixture enhanced extraction of a broader compound range. In orange juice, hexane LLME minimized pulp interference and delivered strong signals for key flavor markers. Comparative chromatograms demonstrated complementary selectivity: DLLME/LLME captured semi-volatile esters and acids, HS-SPME excelled for monoterpenes and small volatiles but showed matrix effects and fiber saturation.

Benefits and practical applications

Automated LLME/DLLME:

• Reduces solvent consumption and manual handling.
• Enables large-volume injection for enhanced sensitivity.
• Offers flexible solvent selection for targeted profiling.

HS-SPME:

• Provides rapid volatile screening without solvent.
• Accessible for routine headspace analysis.

Combined approaches deliver comprehensive flavor maps for research, QA/QC and product development in beverage industries.

Future trends and potential applications

Advances may include integration with comprehensive two-dimensional GC-MS, novel sorbent chemistries for selective microextraction, machine learning algorithms for automated compound identification and green solvent systems to further reduce environmental impact. Miniaturized, fully automated workflows will support real-time monitoring in production lines.

Conclusion

The choice of sample preparation profoundly influences the flavor profile obtained. Automated LLME and DLLME are robust alternatives to HS-SPME for comprehensive aroma analysis. Method optimization by solvent and fiber selection is essential, and quantitative applications will require recovery validation. Laboratories can adopt these workflows for improved efficiency and deeper insights into beverage flavor composition.

References

1. Hutchinson A., Carrier D. AS182: Fully Automated Dispersive Liquid Microextraction for Extractables and Leachables, GlaxoSmithKline & Anatune Ltd.
2. Quick J., Dunscombe J., Stubbs J., Carrier D. AS186: Automated Dispersive Liquid-Liquid Microextraction for EPA 8270, ALS Environmental & Anatune Ltd.
3. Minaeian J., Banks P., Quick J., Stoney P. AS204: Automated Liquid-Liquid Extraction of Epichlorohydrin from Water by GC/MS, Anatune Ltd. & ALS Environmental.