Quick Profiling and Identification of Natural Product Components in Honeysuckle Flower by DDA Method on LCMS-9030 with LabSolutions Insight Explore

Significance of the Topic

The comprehensive profiling and identification of natural product components is critical for quality control, discovery of bioactive compounds and standardization of herbal medicines. High-resolution mass spectrometry (HRMS) combined with data-dependent acquisition (DDA) enables both targeted and untargeted screening, providing accurate mass measurements and rich MS/MS spectra for structural elucidation.

Objectives and Study Overview

This study aims to demonstrate a rapid DDA-based workflow on the Shimadzu LCMS-9030 Q-TOF system to profile and identify main constituents in honeysuckle flower (Lonicera japonica) extract. Data analysis is performed using LabSolutions Insight Explore software to screen known targets, confirm identities via library search, and propose structures for unknowns.

Methodology

• Sample preparation: 1 g dried honeysuckle flower ground under liquid nitrogen; 100 mg extracted with water/methanol (1:1) at 45 °C for 45 min; filtered through 0.22 µm PTFE.
• LC conditions: Nexera UHPLC with Shim-Pack GIST-HP C18 (3.0×150 mm, 3 µm); flow 0.4 mL/min; gradient 15→44→95→15 % acetonitrile with 0.1 % formic acid over 30 min; oven at 40 °C; 2 µL injection.
• MS conditions: Heated ESI positive mode; interface 300 °C; DL 250 °C; heat block 400 °C; nebulizing gas 3 L/min; drying/heating gas 10 L/min; MS range m/z 250–800; DDA range m/z 100–800 with collision energy 18–52 V; preferred precursor list of 639 ions; intensity threshold 10 000; loop time 0.2 s; DDA delay 3 s.

Instrumentation

• Shimadzu LCMS-9030 Q-TOF system
• Nexera UHPLC with Shim-Pack GIST-HP C18 column
• LabSolutions Insight Explore data analysis software

Main Results and Discussion

• A total of 1 209 precursor ions with DDA spectra were detected in the honeysuckle extract.
• Targeted screening of an 85-compound list yielded 250 hits, including chlorogenic acid isomers (3-, 4- and 5-O-caffeoylquinic acids) differentiated by retention time.
• Flavonoid glycosides such as rutin, hyperoside and quercetin were identified via NIST library match; neutral loss behavior during ESI was noted.
• Thirty-three compounds spanning nucleosides, phenolic acids, iridoids and flavonoids were summarized with accurate m/z, retention times and fragment ions.
• Unknown precursors (e.g., m/z 397.1111 and 403.1605) were processed through Formula Predictor and Assign modules; plausible structures were proposed but require further confirmation.
• The workflow integrates Analyze, Screen, Precursor, Library Search, Formula Predictor and Assign tools for streamlined, in-depth profiling and tentative identification.

Benefits and Practical Applications of the Method

• Rapid, high-throughput profiling of complex herbal extracts.
• Accurate mass and MS/MS data support both targeted screening and discovery of unknowns.
• Preferred-ion DDA strategy enhances spectral coverage of key compounds.
• Integrated software modules facilitate data management, annotation and library matching.

Future Trends and Potential Applications

• Adoption of data-independent acquisition (DIA) and ion mobility separation for improved isomer resolution.
• Expansion of high-resolution spectral libraries and AI-driven annotation algorithms.
• Integration with chemometric tools for quality control and batch-to-batch consistency.
• Real-time screening and feedback during chromatographic runs.

Conclusion

The optimized DDA workflow on LCMS-9030 Q-TOF combined with LabSolutions Insight Explore provides an efficient platform for profiling and tentative identification of natural product components in honeysuckle flower. While targeted screening and library matching yield rapid identification, isomer differentiation and unknown structure confirmation require additional retention-time data and orthogonal techniques.

References

1. Qi L.W., Chen C.Y., Li P., Rapid Commun. Mass Spectrom., 2009, 23, 3227–3242.
2. Hao J., Wu Z., Li W., Zhang Z., Gu J., J. Tradit. Chin. Med., 2022, 5, 199–202.
3. Shang X., Pan H., Li M., Miao X., Ding H., J. Ethnopharmacol., 2011, 138, 1–21.