High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile
Author: Shunsuke Miyauchi and Takayuki Yuki and Hiroshi Fuji and Kunio Kojima and Tsutomu Yonetani and Ayako Tomio and Takeshi Bamba and Eiichiro Fukusaki
The current study focused on the tea plant (Camellia sinensis) as a target for artificial cultivation because of the variation in its components in response to light conditions. We analyzed its sensory quality by multi-marker profiling using multicomponent data based on metabolomics to optimize the conditions of light and the environment during cultivation. From the analysis of high-quality tea samples ranked in a tea contest, the ranking predictive model was created by the partial least squares (PLS) regression analysis to examine the correlation between the amino-acid content (X variables) and the ranking in the tea contest (Y variables). The predictive model revealed that glutamine, arginine, and theanine were the predominant amino acids present in high-ranking teas. Based on this result, we established a cover-culture condition (i.e., a low-light intensity condition) during the later stage of the culture process and obtained artificially cultured tea samples, which were predicted to be high-quality teas. The aim of the current study was to optimize the light conditions for the cultivation of tea plants by performing data analysis of their sensory qualities through multi-marker profiling in order to facilitate the development of high-quality teas by plant factories.