Characterization of Glycyrrhiza Glabra Extract by Comprehensive Two-dimensional Liquid Chromatography-mass Spectrometry Using Multisegmented Shift Gradients in the Second Dimension

Significance of the Topic

Comprehensive profiling of plant secondary metabolites is critical for quality control, safety assessment and the discovery of bioactive compounds in herbal medicines.
Glycyrrhiza glabra (licorice) contains diverse triterpene saponins and phenolic flavonoids that contribute to antiviral, antioxidant and anti-inflammatory activities.
The complexity and co-elution of these metabolites in one-dimensional separations limit detection and identification, motivating advanced multidimensional approaches.

Objectives and Study Overview

This study aimed to develop and evaluate a comprehensive two-dimensional reversed-phase liquid chromatography (RP-LC×RP-LC) method coupled with photodiode array (PDA) and mass spectrometry (MS) detection for untargeted metabolic profiling of G. glabra extract.
A novel multi-segmented shift gradient (MSG) in the second dimension was introduced to enhance separation orthogonality and expand metabolite coverage.
The performance of MSG was compared to conventional full in-fraction and simpler shift gradient modes.

Methodology and Instrumentation

Sample Preparation:

• 1 g of ground licorice root extracted with ethanol/water (1:1) by sonication for 60 min, followed by 10 h of dark incubation and filtration.
• Diluted filtrate injected into 1D and 2D LC systems.

Chromatographic Conditions:

• First Dimension (1D):
• Microbore cyano column (150 × 1.0 mm, 2.7 µm); gradient from 35% to 100% acetonitrile over 80 min; flow 10 µL/min.
• Second Dimension (2D):
• Superficially porous C18 column (50 × 2.1 mm, 2.7 µm); flow 0.8 mL/min; 90 s modulation.
• Multi-segmented shift gradient divided into three narrow organic segments with increasing steepness to maximize orthogonality.

Detection:

• PDA recording 210–400 nm at 12.5 Hz.
• ESI-MS in positive/negative modes, m/z 100–1000, with 250 °C interface temperatures and optimized gas flows.

Used Instrumentation

LC×LC System: Shimadzu Nexera-e
Software: Shimadzu LabSolutions v5.65, Chromsquare v2.2, LC×LC-Assist v2.0
PDA Detector and ESI-MS modules as specified in methods.

Main Results and Discussion

Comparison of one-dimensional LC-PDA-MS with RP-LC×RP-LC (MSG) revealed:

• 1D analysis detected ~66 components with significant peak overlap.
• RP-LC×RP-LC-PDA-MS under MSG identified ~120 metabolites, nearly doubling coverage.
• 2D contour plots showed broad peak distribution with minimal correlation between dimensions, confirming high orthogonality.
• Major classes detected included flavanones, flavones, chalconoids and triterpene saponins, with isobaric separation enabled by orthogonal gradients.

The segmented design of the MSG allowed fine tuning of solvent strength in each fraction, improving separation of closely related compounds by exploiting slight differences in hydrophobicity and retention behavior.

Benefits and Practical Applications of the Method

This RP-LC×RP-LC-PDA-MS approach with MSG offers:

• Enhanced metabolite coverage for complex botanical extracts.
• Improved resolution of isobaric and co-eluting compounds.
• Deeper insight into phytochemical profiles for research, quality assurance and standardization.
• Potential for rapid screening of bioactive constituents in natural product discovery.

Future Trends and Potential Applications

Anticipated developments include:

• Integration with high-resolution mass spectrometry for precise identification and quantitation.
• Automation of gradient optimization and data processing with machine learning tools.
• Extension of MSG strategies to other orthogonal combinations (e.g., HILIC×RPLC) for diverse sample matrices.
• Application in metabolomics, food safety testing and plant breeding programs.

Conclusion

The introduction of a multi-segmented shift gradient in RP-LC×RP-LC-PDA-MS significantly increases separation orthogonality and metabolite coverage in Glycyrrhiza glabra extract analysis.
This method doubled the number of detected compounds compared to 1D-LC, enabling comprehensive characterization of flavonoids and saponins.
The approach is versatile for profiling complex botanical matrices in research and industrial quality control settings.