Surface-enhanced Raman spectroscopy-based metabolomics for the discrimination of Keemun black teas coupled with chemometrics

Abstract

In the present study, the Surface-enhanced Raman Spectroscopy (SERS)-based metabolomics approach coupled with chemometrics was developed to determine the geographic origins of Keemun black tea. The SERS peaks enhanced by Ag nanoparticles at Δv = 555, 644, 731, 955, 1240, 1321, and 1539 cm−1 were selected, and the intensities were calculated for chemometric analysis. Linear discriminant analysis (LDA) presented an average discrimination accuracy of 86.3%, with 84.3% cross-validation for evaluation. The recognition of three machine learning algorithms, namely feedforward neural network (FNN), random forest (RF), and K-Nearest Neighbor (KNN), for black tea were 93.5%, 93.5%, and 87.1%, respectively. Herein, this study demonstrates the potential of the SERS technique coupled with AgNPs and chemometrics as an accessible, prompt, and fast method for discriminating the geographic origins of teas.