Rapid selection of theanine-rich green tea (Camellia sinensis L.) trees and metabolites profiling by Fourier transform near-infrared (FT-IR) spectroscopy

Abstract

A simple and rapid ninhydrin-based colorimetric method was efficiently developed, and metabolites profiling of tea tree (Camellia sinensis L.) leaf extracts was carried out by Fourier transform near-infrared (FT-IR) spectroscopy. The tea extracts of Hadong region exhibited a wide range of variations in their theanine contents. In general, theanine and other amino acids were higher in classified theanine-rich lines than that of theanine-poor lines. Among selected theanine-rich lines, the theanine levels reached a maximum of 15.3 mg/g for line TR92, a value 51-fold more than that found in theanine-poor TP23 and TP156 lines. Therefore, ninhydrin-based colorimetric method can be efficiently adopted for the selection of theanine-rich plants thereby enabling selection of plants dependent on their chemical constituents. FT-IR spectroscopy analysis was conducted in 15 tea tree lines as theanine-rich 8 lines and -poor 7 lines. The spectral data were analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). PCA and PLS-DA could successfully discriminate theanine-rich and -poor lines. The quantitative prediction modeling of total amino acids and theanine from tea tree leaf lines was established using partial least square regression algorithm from FT-IR spectra. The regression coefficients (R (2)) between predicted values and estimated values of total amino acids and theanine were 0.976 and 0.992, respectively. These results showed that quantitative predictions of total amino acids and theanine were possible from FT-IR spectra of tea tree leaf lines with higher accuracy. These results suggest that the prediction system established in this study could be applied as a rapid selection of theanine-rich lines of tea tree.