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J Biosci Bioeng. 2014 Dec;118(6):710-5. doi: 10.1016/j.jbiosc.2014.05.008. Epub 2014 Jun 7.

High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile.

Author information

1
Corporate Research and Development Division, Sharp Corporation, 2613-1 Ichinomoto-cho, Tenri, Nara 632-8567, Japan.
2
Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
3
Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: fukusaki@bio.eng.osaka-u.ac.jp.

Abstract

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.

KEYWORDS:

Camellia sinensis; Metabolomics; Plant factory; Prediction model; Quality of green tea

PMID:
24915994
DOI:
10.1016/j.jbiosc.2014.05.008
[Indexed for MEDLINE]

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