Detection of Gegen Adulteration Using Multiple Fingerprints Coupled With Chemometric Strategy

Abstract

Background The root of Pueraria montana var. lobata (Gegen) was a well-known traditional Chinese medical herb, which was prone to be accidentally contaminated with Pueraria montana var. thomsonii, Pueraria wallichii, and Pueraria peduncularis due to the morphological character similarity. These adulterations might cause quality confusion and safety issues. Objective In this study, the screening technique to detect adulteration in Gegen was developed using multiple fingerprints and chemometrics. Method A range of Gegen samples and possible known adulterants including Pueraria montana var. thomsonii, Pueraria wallichii, and Pueraria peduncularis were collected. FT-IR and HPLC coupled with different chemometric techniques, including similarity analysis (SA), hierarchical clustering analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), provide the qualitative chemometric models for Gegen adulteration detection. Results FT-IR and HPLC combined with OPLS-DA successfully differentiated authentic Gegen from adulterants. Both FT-IR and HPLC units can be used as alternative methods to traditional methods. The HPLC showed better performance in identifying samples than FT-IR. Conclusions The use of FT-IR and HPLC coupled with chemometrics could potentially be the proper selection method for the early quality evaluation of Gegen. This method can be used to combat fraud in the herbal industry in the future. Highlights FT-IR and HPLC combined with chemometrics analysis were developed to discriminate between Pueraria lobata (Gegen) and adulterants. The multiple fingerprints combined with multivariate methods were successfully applied to the study of the Gegen and its adulterants. The chemometrics analysis using SA and OPLS-DA indicate significant differentiation in the chemical composition of these species. This research provides important chemotaxonomic references in species identification.