應用近紅外線光譜技術於台灣部分發酵茶之品種、產地與產季的鑑別   全文下載

Classifying the Variety, Production Area and Season of Taiwan Partially Fermented Tea by Near Infrared Spectroscopy

本研究主要目的在探討應用近紅外線光譜技術（Near Infrared spectros-copy, NIRS）於台灣部分發酵茶之品種、產地與產季的鑑別之可行性。利用共308件來自6個不同品種、6個不同產地與2個不同產季的台灣部分發酵茶作為試驗材料。結果顯示利用近紅外線光譜配合主成分分析（principal com-ponent analysis, PCA）所累積之前三個主成分可解釋茶樣95%之變異，其中區分不同產地之茶樣效果最佳，不同品種茶樣之效果次佳。利用近紅外線光譜搭配部分最小迴歸分析（partial least square, PLS）所得之鑑別模式，鑑別不同品種與不同產地的成功率分別為98.4%（299 of 305茶樣）與97.4%（296 of 304茶樣）。另利用所得之鑑別模式更可以100% 正確鑑別不同產季之茶樣。以此鑑別模式進一步鑑別不同品種、不同產地與不同產季的茶樣，成功率分別為96.3% 、94.1%與99.2%，鑑別成功率與原本模式相當。由此可見利用近紅外線光譜所製得之鑑別模式，能有效鑑別不同品種、產地與產季之台灣部分發酵茶。"

The purpose of this study is to investigate the feasibility of discriminating the different varieties, production areas and seasons of Taiwan partially fermented tea by using Near Infrared Spectroscopy (NIRS). A total of 308 partially fermented tea samples with 6 different tea varieties, 6 production areas and 2 different production seasons were collected and analyzed. The principal compo-nent analysis (PCA) result of NIRS spectra data showed that the first three principal components could explain the sample variation up to 95.0%. The ability of classifying different production areas of tea samples by PCA was the best followed by tea varieties. The discriminant model further established by NIRS data with partial least square (PLS) could recognize and identify the varieties, production areas and seasons of tea samples up to 98.4% (299 of 305), 97.4% (296 of 304), and 100%, respectively. Using the established discriminant model, the tea samples with different varieties, production areas and seasons could be correctly predicted and identified at the levels of 96.3%, 94.1% and 99.2%, respectively.