Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity. •The combination of near infrared spectrum (NIR) technology and chemometric methods was proposed for simple and rapid distinguishment of three classes of G. elata Bl. They are G. elata Bl. f. glauca (black G. elata Bl.), G. elata Bl. f. elata (red G. elata Bl.) and their hybrid.•After NIR data acquisition of samples by using optimal experimental conditions, the strategies for optimal construction of models were attained, including second-order derivative, Norris method for smoothing, MC-UVE for wavelength screening, and PLS-DA for modelling.•The LOO cross-validation method was applied to divide data of training and test sets. It shows good performance with prediction accuracy of 99.11% and 98.89% to training and test sets, respectively.