1H NMR Based Metabolic Profiling in the Evaluation of Japanese Green Tea Quality

Lucksanaporn Tarachiwin†‡, Koichi Ute§, Akio Kobayashi‡ and Eiichiro Fukusaki*‡
Nara Prefectual Small and Medium Sized Enterprises Support Corporation, Nara, Japan, Department of Biotechnology, Graduate School of Engineering, Osaka University, Osaka, Japan, and Department of Chemical Science and Technology, Faculty and School of Engineering, The University of Tokushima, Tokushima, Japan
J. Agric. Food Chem., 2007, 55 (23), pp 9330–9336
DOI: 10.1021/jf071956x
Publication Date (Web): October 19, 2007
Copyright © 2007 American Chemical Society
†

Nara Prefectual Small and Medium Sized Enterprises Support Corp.

,
‡

Osaka University.

,
§

The University of Tokushima.

,
* To whom correspondence should be addressed. Tel: +81-6-6879-7424 . Fax: +81-6-6879-7424. E-mail: fukusaki@bio.eng.osaka-u.ac.jp.

Abstract

Classification of tea quality is now mainly performed according to the sensory results by professional tea tasters. However, this evaluation method is inconsistent in differentiating their qualities. A combination of a 1H NMR technique and a multivariate analysis was introduced to the quality evaluation of green tea by means of a metabolomic technique. A broad range of metabolites were detected by 1H NMR spectrometry. The principal component analysis (PCA) was used to reduce the complexity of the 1H NMR spectra data set and provided the quality discrimination result. It offered an extensive clue for classification and quality assessment without any prepurification method. A set of green teas from a Japanese tea contest were analyzed by 1H NMR to classify the quality with respect to that judged by tea tasters and to conceive a quality prediction model. Metabolic profiling and fingerprinting of 1H NMR spectra of green teas with different quality were studied. PCA showed a separation between the high- and the low-quality green teas. The taste marker compounds contributing to the discrimination of tea quality were identified. Reliable prediction models were obtained by the partial least-squares projection to latent structure (PLS) analysis together with a preprocessing filter of both orthogonal signal correction (OSC) and a combination between OSC and wavelet transform algorithms.

View: Full Text HTML | Hi-Res PDF

Tools

History

  • Published In Issue November 14, 2007
  • Article ASAPOctober 19, 2007
  • Received: July 01, 2007
    Accepted: September 24, 2007
    Revised: September 21, 2007

Recommend & Share

Related Content

Other ACS content by these authors: