Multivariate analysis for 1 H-NMR spectra of two hundred kinds of tea in the world

Masako Fujiwara, Itiro Ando, Kazunori Arifuku

研究成果: Article査読

29 被引用数 (Scopus)

抄録

NMR measurements coupled with pattern-recognition analysis offer a powerful mixture-analysis tool for latent-feature extraction and sample classification. As fundamental applications of this analysis for mixtures, the 1 H spectra of 176 kinds of green, black, oolong and other tea infusions were acquired by a 500 MHz NMR spectrometer. Each spectrum pattern was analyzed by a multivariate statistical pattern-recognition method where Principal Component Analysis (PCA) was used in combination with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA effectively selected variables that contribute to tea categorization. The final PCA resulted in clear classification reflecting the fermentation and processing of each tea, and revealed marker variables that include catechin and theanine peaks. 2006

本文言語English
ページ(範囲)1307-1314
ページ数8
ジャーナルanalytical sciences
22
10
DOI
出版ステータスPublished - 2006 10

ASJC Scopus subject areas

  • 分析化学

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