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

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 & UDelta;v = 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.

Automated summary

In this study, a Surface-enhanced Raman Spectroscopy (SERS) and chemometric-based metabolomics approach was developed to determine the geographic origins of Keemun black tea. SERS peaks enhanced by Ag nanoparticles were selected and intensities were calculated for chemometric analysis. Linear discriminant analysis (LDA) achieved an average discrimination accuracy of 86.3% and cross-validation of 84.3%, while three machine learning algorithms, feedforward neural network (FNN), random forest (RF), and K-Nearest Neighbor (KNN), achieved recognition rates of 93.5%, 93.5%, and 87.1% respectively. This study demonstrates the potential of using SERS technique coupled with AgNPs and chemometrics as a reliable, fast, and convenient method for determining the geographic origins of teas.