Abstract

The metabolic profiles of Panax notoginseng and its associated therapeutic values are critically affected by the duration of steaming. The time-dependent steaming effect of P. notoginseng is not well-characterized and there is also no official guideline on its duration of steaming. In this paper, a UHPLC/TOFMS-based metabolomic platform was developed for the qualitative profiling of multiparametric metabolic changes of raw P. notoginseng during the steaming process. Our method was successful in discriminating the differentially processed herbs. Both the unsupervised principal component analysis (PCA) score plot ( R 2 X = 0.664, Q 2 (cum) = 0.622, and PCs = 2) and the supervised partial least square-data analysis (PLS-DA) model ( R 2 X = 0.708, R 2 Y = 0.461, and Q 2 Y = 0.271) demonstrated strong classification and clear trajectory patterns with regard to the duration of steaming. The PLS-DA model was validated for its robustness via a prediction set, confirming that the UHPLC/TOFMS metabolic profiles of the raw and differentially steamed P. notoginseng samples were highly reproducible. Based on our method, the minimum durations of steaming for the maximum production of bioactive ginsenosides such as Rg3 and Rh2 were also predicted. Our novel time-dependent metabolic profiling approach represents the paradigm shift in the quality control of P. notoginseng products.

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