Quality evaluation of green tea leaf cultured under artificial light condition using gas chromatography/mass spectrometry.

For an experimental model to elucidate the relationship between light quality during plant culture conditions and plant quality of crops or vegetables, we cultured tea plants (Camellia sinensis) and analyzed their leaves as tea material. First, metabolic profiling of teas from a tea contest in Japan was performed with gas chromatography/mass spectrometry (GC/MS), and then a ranking predictive model was made which predicted tea rankings from their metabolite profile. Additionally, the importance of some compounds (glutamine, glutamic acid, oxalic acid, epigallocatechin, phosphoric acid, and inositol) was elucidated for measurement of the quality of tea leaf.

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

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).

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions.

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer.

High-throughput technique for comprehensive analysis of Japanese green tea quality assessment using ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS).

Applications of metabolomics techniques along with chemometrics provide an understanding in the relationship between metabolome of green tea and its quality. A coupled of ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS) allowed a high-throughput and comprehensive analysis with minimal sample preparation. Using this technique, a wide range of metabolites were investigated.

Quality prediction of Japanese green tea using pyrolyzer coupled GC/MS based metabolic fingerprinting.

A couple between pyrolyzer and gas chromatography/mass spectrometry (GC/MS) has allowed a fast, simple, and low-cost approach to evaluate a quality of Japanese green tea without any sample preparation or derivatization techniques. Using our method, errors from sample preparation could be avoided since raw samples were directly extracted through the extreme heat of the pyrolyzer. In addition, undesired reactions from expensive derivatizing agents, which are commonly needed to treat the samples before analyzing with GC/MS, could be omitted.

Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy.

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes.

Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting.

An innovative technique for green tea's quality determination was developed by means of metabolomics. Gas-chromatography coupled with time-of-flight mass spectrometry and multivariate data analysis was employed to evaluate the quality of green tea. Alteration of green tea varieties and manufacturing processes effects a variation in green tea metabolites, which leads to a classification of the green tea's grade.