Discriminant Analysis of the Geographical Origins of Taiwanese oolong Tea using Surface-Enhanced Raman Spectroscopy

• Main Authors: Yu-Wei Liao, 廖育偉
• Other Authors: Shih-Fang Chen
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/4j9725

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 105 === Oolong tea, one of the semi-oxidized teas, is a high profitable tea types and has a large market share in Taiwan. The geographic origin of oolong tea is one of the major factors for its market price due to the special flavor of high-mountain tea, which taste sweet and fresh. Therefore, the price of tea from high-mountain is higher than from low-elevation area. Surface-enhance Raman scattering (SERS) is a novel spectroscopic method for compositional analysis, and it is selected in this study to develop classification models to identifying the locations, seasons and elevations of oolong tea. Tea samples used in this study were from seven locations: Nantou, Taoyuan, Pinglin, Dayuling, Lishan, Ali and Senlin. Besides, all of samples were collected in spring and winter, and the elevations were defined as elementary, intermediate and superior. There were 14 physicochemical parameters were measured to describe the properties of physical and chemical of tea. Among of these parameters, free amino acid, total polyphenol, GCG, ECG, GC were used to discriminate the locations that reached statistical significance. Using Raman spectroscopy, the fingerprint spectra of oolong tea was developed. The locations of five featured Raman peaks were identified including theophylline, theobromine, caffeine, catechins, and L-theanine. Slightly compositional differences on Raman spectra of different origins were observed but there is no statistical significance. Soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) and support vector machine (SVM) were adopted to develop the classification models with SERS spectra. SIMCA performed a better accuracy for classification than others. The accuracy for locations, seasons, and altitudes were 85%, 75%, and 80%, respectively. A predictive model was developed for identifying the geographical origins of oolong tea in Taiwan using SERS and multivariate methods.