Discriminant Analysis of the Geographical Origins of Oolong Tea Using Surface-Enhanced Raman Spectroscopy

Abstract. Oolong tea is a highly profitable type of tea and has a large market share in Taiwan. Due to the special flavor of high-mountain tea, the geographical origin of oolong tea is one of the major factors for its market price. Surface-enhanced Raman spectroscopy (SERS) is a novel spectroscopic method for compositional analysis. It was selected in this study to develop classification models for identifying the locations, seasons, and altitudes of oolong tea. The tea samples used in this study were from five locations: Nantou, Dayuling, Ali, Senlin, and Taoyuan. All the samples were collected in spring and winter. Elevations were defined as elementary, intermediate, and superior. Principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) were adopted. Relative to PCA, SIMCA provided higher accuracy for the classification of locations, seasons, and elevations at 81.8%, 72.7%, and 81.8%, respectively. A predictive model was developed for identifying the geographical origins of high-mountain oolong tea in Taiwan using SERS and multivariate methods.