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High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile.
J Biosci Bioeng. 2014 Dec; 118(6):710-5.JB

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.

Authors+Show Affiliations

Corporate Research and Development Division, Sharp Corporation, 2613-1 Ichinomoto-cho, Tenri, Nara 632-8567, Japan.Corporate Research and Development Division, Sharp Corporation, 2613-1 Ichinomoto-cho, Tenri, Nara 632-8567, Japan.Corporate Research and Development Division, Sharp Corporation, 2613-1 Ichinomoto-cho, Tenri, Nara 632-8567, Japan.Corporate Research and Development Division, Sharp Corporation, 2613-1 Ichinomoto-cho, Tenri, Nara 632-8567, Japan.Corporate Research and Development Division, Sharp Corporation, 2613-1 Ichinomoto-cho, Tenri, Nara 632-8567, Japan.Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.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.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

24915994

Citation

Miyauchi, Shunsuke, et al. "High-quality Green Tea Leaf Production By Artificial Cultivation Under Growth Chamber Conditions Considering Amino Acids Profile." Journal of Bioscience and Bioengineering, vol. 118, no. 6, 2014, pp. 710-5.
Miyauchi S, Yuki T, Fuji H, et al. High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile. J Biosci Bioeng. 2014;118(6):710-5.
Miyauchi, S., Yuki, T., Fuji, H., Kojima, K., Yonetani, T., Tomio, A., Bamba, T., & Fukusaki, E. (2014). High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile. Journal of Bioscience and Bioengineering, 118(6), 710-5. https://doi.org/10.1016/j.jbiosc.2014.05.008
Miyauchi S, et al. High-quality Green Tea Leaf Production By Artificial Cultivation Under Growth Chamber Conditions Considering Amino Acids Profile. J Biosci Bioeng. 2014;118(6):710-5. PubMed PMID: 24915994.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - High-quality green tea leaf production by artificial cultivation under growth chamber conditions considering amino acids profile. AU - Miyauchi,Shunsuke, AU - Yuki,Takayuki, AU - Fuji,Hiroshi, AU - Kojima,Kunio, AU - Yonetani,Tsutomu, AU - Tomio,Ayako, AU - Bamba,Takeshi, AU - Fukusaki,Eiichiro, Y1 - 2014/06/07/ PY - 2013/12/21/received PY - 2014/05/03/revised PY - 2014/05/09/accepted PY - 2014/6/12/entrez PY - 2014/6/12/pubmed PY - 2015/5/20/medline KW - Camellia sinensis KW - Metabolomics KW - Plant factory KW - Prediction model KW - Quality of green tea SP - 710 EP - 5 JF - Journal of bioscience and bioengineering JO - J Biosci Bioeng VL - 118 IS - 6 N2 - 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. SN - 1347-4421 UR - https://www.unboundmedicine.com/medline/citation/24915994/High_quality_green_tea_leaf_production_by_artificial_cultivation_under_growth_chamber_conditions_considering_amino_acids_profile_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1389-1723(14)00161-3 DB - PRIME DP - Unbound Medicine ER -