Comparative investigation for raw and processed Aconiti Lateralis Radix using chemical UPLC-MS profiling and multivariate classification techniques

Abstract

A strategy combining chemical UPLC-MS profiling and multivariate classification techniques has been used for the comparison of raw and processed Aconiti Lateralis Radix. UPLC-MS was used to identify 18 characteristic compounds, which were selected for discrimination of the raw and two processed products (Heishunpian and Baifupian). Chemometric analyses, including the combination of a heat map and hierarchical cluster analysis (HCA) and principal component analysis (PCA), were used to visualize the discrimination of raw and two processed products. HCA and PCA provided a clear discrimination of raw Aconiti Lateralis Radix, Heishunpian and Baifupian. Finally, the counter-propagation artificial neural network (CP-ANN) was applied to confirm the results of HCA, PCA and to explore the effect of 18 compounds on samples differentiation and the rationality of processing. The results showed that this strategy could be successfully used for comparison of raw and two processed products of Aconiti Lateralis Radix, which could be used as a general procedure to compare herbal medicines and related processed products to elaborate the rationality of processing from the perspective of chemical composition.

Keywords: Aconiti lateralis radix; Classification; Counter propagation artificial neural network; Processed rationality; UPLC-MS profiling.