A proposed protocol based on integrative metabonomics analysis for the rapid detection and mechanistic understanding of sulfur fumigation of Chinese herbal medicines

Abstract

In the current work, Lonicera japonica Flos (FLJ) was selected as a model Chinese herbal medicine (CHM) and a protocol was proposed for the rapid detection of sulfur-fumigated (SF) CHMs. A multiple metabonomics analysis was conducted using HPLC, NIR spectroscopy and a UHPLC-LTQ-Orbitrap mass spectrometer. First, the group discriminatory potential of each technique was respectively investigated based on PCA. Then, the effect of mid-level metabonomics data fusion on sample spatial distribution was evaluated based on data obtained using the above three technologies. Furthermore, based on the acquired HRMS data, 76 markers discriminating SF from non-sulfur-fumigated (NSF) CHMs were observed and 49 of them were eventually characterized. Moreover, NIR absorptions of 18 sulfur-containing markers were identified to be in close correlation with the discriminatory NIR wavebands. In conclusion, the proposed protocol based on integrative metabonomics analysis that we established for the rapid detection and mechanistic explanation of the sulfur fumigation of CHMs was able to achieve variable selection, enhance group separation and reveal the intrinsic mechanism of the sulfur fumigation of CHMs.

Fig. 1. The results of primary metabonomics data fusion analysis. (A–C) PCA for HPLC-DAD, NIR and LC-MS; (D–F) PLS-DA for HPLC-DAD, NIR and LC-MS.

Fig. 2. The results of mid-level metabonomics data fusion analysis. (A–D) Mid-level metabonomics data fusion analysis without variable selection for NIR-HPLC, HPLC-MS, NIR-MS and NIR-HPLC-MS. (E–H) Mid-level metabonomics data fusion analysis with variable selection for NIR-HPLC, HPLC-MS, NIR-MS and NIR-HPLC-MS.

Fig. 3. The mass fragmentation behaviors of identified markers. (A) HRMS¹ spectrum of M14. (B) ESI-MS² spectrum of M20. (C) HRMS¹ spectrum of M23. (D) ESI-MS² spectrum of M24. (E) The S-plot of LC-MS metabonomics analysis.

Fig. 4. The MOCA for the FLJ. (A) The discriminatory information of preprocess method of SG9+1^st; (B) the synchronous 2D-COS auto-peak analysis of the SF and NSF samples; (C) the mass fragmentation behaviors of SF chlorogenic acid; (D) the synchronous 2D-COS auto-peak analysis of the SF and NSF chlorogenic acid samples; (E) the mass fragmentation behaviors of SF chlorogenic acid.

Fig. 5. A suggested protocol for the rapid discrimination of SF CHMs and an explanatory mechanism.

Fig. 6. Comparison between the unique model and the metabonomics data fusion model. (A–C) PCA for HPLC-DAD, NIR and LC-MS. (D–G) Result for HPLC-NIR, HPLC-MS, NIR-MS and NIR-HPLC-MS data fusion without variable selection analysis. (H–K) Result for HPLC-NIR, HPLC-MS, NIR-MS and NIR-HPLC-MS data fusion with variable selection analysis.