Abstract
A rapid and sensitive approach to differentiate sulfur-fumigated (SF) Ophiopogonis Radix based on Multi-Omics Correlation Analysis (MOCA) strategy was first established. It was characterized by multiple data-acquisition methods (NIR, HPLC, and UHPLC-HRMS) based metabonomics and multivariate statistical analysis methods. As a result, SF and non-sulfur fumigated (NSF) Ophiopogonis Radix samples were efficaciously discriminated. Moreover, based on the acquired HRMS data, 38 sulfur-containing discriminatory markers were eventually characterized, whose NIR absorption could be in close correlation with the discriminatory NIR wavebands (5000–5200 cm−1) screened by NIR metabonomics coupled with SiPLS and 2D-COS methods. This results were also validated from multiple perspectives, including metabonomics analysis based on the discriminatory markers and the simulation of SF ophiopogonin D and Ophiopogonis Radix sample. In conclusion, our results first revealed the intrinsic mechanism of discriminatory NIR wavebands by means of UHPLC-HRMS analysis. Meanwhile, the established MOCA strategy also provided a promising NIR based differential method for SF Ophiopogonis Radix, which could be exemplary for future researches on rapid discrimination of other SF Chinese herbal medicines.
Highlights
Most Chinese herbal medicines (CHM) need to undergo post-harvest processing to convert raw materials into ready-to-use forms[1]
It has recently emerged as a controversial topic due to the potential detrimental effects to CHM, such as chemical transformation of inherent herbal constituents that result in changing bioactivities, pharmacokinetics, and toxicities[3,4,5,6]
It is of great importance to develop a rapid and sensitive approach to ascertain the sulfur-fumigation state of a given medicinal herb for CHM quality control
Summary
No clear separation between SF and NSF samples was obtained using the established PCA model based on the preprocess method of SG9 + 1st. Discrimination of SF and NSF samples using HPLC-DAD/ELSD based metabonomics analysis. The PLS-DA model (Fig. S4A) resulted in a clear separation of SF and NSF samples with R2(Y) of 81.9% and Q2 of 61.1%, which demonstrated the model is statistically significant. In order to make up to the drawback of HPLC-DAD/ELSD analysis, UHPLC-LTQ-Orbitrap HRMS instrument was employed to discriminate the SF and NSF samples. The constituents detected by UHPLC-LTQ-Orbitrap HRMS and fulfilled the three criteria that are S plot (p > 0.05 and p (corr) > 0.3), VIP value (>1.5) and t test (p < 0.05) were considered as the most relevant chemicals in discrimination of these two groups (Fig. 4C was the S-plot figure). Raw* MSC SNV Baseline Normalization S-T WDS S-G(9) + 1st S-G(11) + 1st S-G(9) + 2nd S-G(11) + 2nd
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